research article histogram of oriented gradient based gist

Research ArticleHistogram of Oriented Gradient Based Gist Featurefor Building Recognition

Bin Li1 Kaili Cheng1 and Zhezhou Yu2

1School of Information Engineering Northeast Electric Power University Jilin 132012 China2School of Computer Science and Technology Jilin University Changchun 130012 China

Correspondence should be addressed to Bin Li libinjlu5765114163com

Received 26 June 2016 Revised 19 September 2016 Accepted 10 October 2016

Academic Editor Rodolfo Zunino

Copyright copy 2016 Bin Li et al This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

We proposed a new method of gist feature extraction for building recognition and named the feature extracted by this method asthe histogram of oriented gradient based gist (HOG-gist)The proposedmethod individually computes the normalized histogramsof multiorientation gradients for the same image with four different scales The traditional approach uses the Gabor filters withfour angles and four different scales to extract orientation gist feature vectors from an image Our method in contrast uses thenormalized histogram of oriented gradient as orientation gist feature vectors of the same image These HOG-based orientationgist vectors combined with intensity and color gist feature vectors are the proposed HOG-gist vectors In general the HOG-gistcontains four multiorientation histograms (four orientation gist feature vectors) and its texture description ability is stronger thanthat of the traditional gist using Gabor filters with four angles Experimental results using Sheffield Buildings Database verify thefeasibility and effectiveness of the proposed HOG-gist

1 Introduction

Building recognition is becoming increasingly more interest-ing to researchers since it can be applied to many real-worldproblems such as robot vision or localization [1] mobiledevice navigation [2 3] and building labeling in videosHowever building recognition is a challenging task becausebuilding images could be taken from different viewpointsunder different lighting conditions or suffering from occlu-sion from billboard trees vehicles or other buildings Thebiggest difficulty for build recognition is to design a featureextraction algorithm that can accurately and completelydescribe building characteristics

Interest points extracted by the Harris corner detectorwere applied to matching buildings in the world space formobile device [3] Li and Shapiro [4] used the consistentline cluster for content-based image retrieval Specificallythe color orientation and spatial features of line segmentsare exploited to group image into line clusters The intra-cluster and intercluster relationships were used to recognizebuildings in photographic images Zhang and Kosecka [5]

proposed a hierarchical building recognition method that hastwo steps and bases on the localized color histograms Thefirst step uses localized color histograms and in the secondstep themethod refinedmatching SIFTdescriptors Fritz et al[6] applied the ldquoInformative Descriptor Approachrdquo on SIFTfeatures (i-SIFT descriptors) and proposed a robust buildingdetection and recognition method

In [7] Li and Allinson pointed out the following all thementioned building recognition algorithms have two draw-backs (1) They are based on the detection of low-level fea-tures such as vanishing points and line segments The repre-sentation of building characteristics is restricted since theselow-level features cannot reveal the truly semantic conceptsof building images (2) If these raw high-dimensional featurevectors were used for recognition which may cause largememory requirements it would result in high computationalcost Li and Allinson proposed a new building recognitionmethod to address these two drawbacks Li and Allinson usethe gist feature extraction approach proposed by Siagian andItti to obtain gist features of building images In Siagian andIttirsquos gist extractionmethod [8] 34 featuremaps are created by

Hindawi Publishing CorporationComputational Intelligence and NeuroscienceVolume 2016 Article ID 6749325 9 pageshttpdxdoiorg10115520166749325

2 Computational Intelligence and Neuroscience

filtering of the original image in orientation channels colorchannels and intensity channels in multiple spatial scalesEach feature map is divided into a 4 times 4 grid and the meanvalues of each grid were calculated to produce 16 values fora gist vector As a result the original image is representedby a 544-dimension feature vector To reduce computationalcosts and preserve discriminative information as much aspossible several manifold learning dimensionality reductionalgorithms such as principal component analysis (PCA)[9] locality preserving projections (LPP) [10] and lineardiscriminant analysis (LDA) [11] are used for dimensionalityreduction before recognition The gist feature extractionand dimensionality reduction-based building recognitionmethod proposed by Li and Allinson has been proven to bemore effective than those low-level feature methods [3ndash7] Liet al [12] proposed subregionrsquos multiscale gist feature (SM-gist) extraction method The SM-gist divided building imageinto 4times5 subregions and gist vectors are extracted from thesesubregions individually The interference of nonuniformillumination is mitigated by the SM-gist extraction methodZhao et al [13] proposed multiscale gist (MS-gist) featurefor building recognition The MS-gist features can be stableto capture the representation features of the building imageswith rotation variant lighting conditions and occlusions

The gist feature extraction methods proposed by Siagianand Itti were originally used for the task of scene recognitionbut building recognition is different from scene recognitionThis is because there are many lines on the building sur-face For building recognition tasks the texture feature ofbuildings is more important than the color and intensityfeatures Siagian and Ittirsquos gist feature extraction method usedGabor filters with only four angles to extract the orientationinformation So the texture description ability of Siagianand Ittirsquos gist feature extraction method is not good Toimprove the texture description ability of Siagian and Ittirsquosgist feature extraction method we propose histogram oforiented gradient based gist (HOG-gist) feature extractionmethod The histogram of oriented gradient (HOG) wasfirst proposed by Dalal and Triggs [14] Due to the strongtexture and shape description ability the HOG can be usedin human detection [14] face recognition [15 16] imageregistration [17] and many other tasks [18ndash21] Our pro-posed HOG-gist extraction method individually computesthe normalized histograms of multiorientation gradients forthe same image with four different scales These normalizedhistograms of oriented gradients are orientation gist featurevectors of an image These orientation gist vectors combinedwith intensity and color gist feature vectors proposed bythe traditional method are the proposed HOG-gist vec-tors

This paper is organized as follows we give a brieflyreview of Siagian and Ittirsquos gist feature extraction methodin Section 2 the histogram of oriented gradient basedgist (HOG-gist) feature extraction method is proposed inSection 3 recognition performance on the Sheffield BuildingsDatabase is detailed in Section 4 while Section 5 concludesthe paper

2 Gist Feature Extraction

In this section we give a brief review of Siagian and Ittirsquos gistfeature extraction method [8] and the building recognitionmethod proposed by Li and Allinson [7]

The psychological research [22] has proven that humancan grasp the ldquogistrdquo of an image by glancing at it for just afew seconds Siagian and Ittirsquos gist feature extraction methodis aiming to simulate this ability of human beings Siagianand Ittirsquos gist feature extraction method has two main stepssaliency feature map construction and gist feature extractionSaliency feature maps are constructed based on low-levelvisual features including the intensity channel color channeland orientation channel which are extracted in parallelEquation (1) is utilized to compute the intensity channel[8 23]

119868 = (119903 + 119892 + 119887) (1)

119877 119866 119861 119884 color channel [8 23] can be obtained by thefollowing equations

119877 = 119903 minus (119892 + 119887)2 119866 = 119892 minus (119903 + 119887)2 119861 = 119887 minus (119903 + 119892)2 119884 = (119903 + 119892)2 minus

1003816100381610038161003816119903 minus 11989210038161003816100381610038162 minus 119887

(2)

where 119903 119892 119887 represent the red green and blue channels of theRGB color space of the original image

For the intensity channel and the color channel fiveimage Gaussian pyramids 119868(120590) 119877(120590) 119866(120590) 119861(120590) and 119884(120590)with nine spatial scales ranging from 1 1 (scale zero) to1 256 (scale eight) in eight octaves are created [8 23] where120590 = 0 1 8 The intensity and color saliency feature mapscan be obtained by applying the center-surround operationto these Gaussian pyramids The center-surround operationdefined by Siagian and Itti is as follows [8 23] a pixel at scale119888 = 2 3 4 is the center and the corresponding pixels at scale119904 = 119888 + 120575 where 120575 = 3 4 is the surround From (3) we canget six intensity feature maps [8 23] and twelve color featuremaps [8 23] are obtained by (4)

119868 (119888 119904) = |119868 (119888) ⊝ 119868 (119904)| (3)

119877119866 (119888 119904) = |(119877 (119888) minus 119866 (119888)) ⊝ (119866 (119904) minus 119877 (119904))| 119861 (119888 119904) = |(119861 (119888) minus 119884 (119888)) ⊝ (119884 (119904) minus 119861 (119904))| (4)

where ⊝ denotes the cross-scale difference between twoimages in a Gaussian pyramid

Gabor filters with four different scales 119888 = 1 2 3 4 andfour orientations 120579 = 0∘ 45∘ 90∘ 135∘ are applied to theintensity channel 119868 and extract the 16 orientation featuremaps[8 23]

Computational Intelligence and Neuroscience 3

Linear filtering a four-scale pyramid

Orientation channel0∘ 45

∘

90∘

135∘

4or

ient

atio

ns at

4 d

iffer

ent s

cale

sLinear filtering a nine-scale pyramid

Color channel Intensity channel

Cross scale center-surround difference

34 feature maps

Recognition result

Classifier

Low-dimensionalfeature

Dimensionality reduction

544-dimensiongist feature

Gist featureextraction

RG(c s) BY(c s) I(c s)

Figure 1 The building recognition method proposed by Li and Allinson

In total 34 saliency feature maps are computed 6 forintensity 12 for color and 16 for orientation

Each map is then divided into 4 times 4 grid subregionsand then take the mean of each grid to produce 16 valuesfor the 16-dimension gist feature vector We can get 34 gistfeature vectors from the 34 feature maps The 34 gist featurevectors included 6 intensity gist feature vectors 12 colorgist feature vectors and 16 orientation gist feature vectorsThe combination of all the gist feature vectors is a 544-dimension feature vector Therefore each building imagecan be represented by this 544-dimension feature vectorFigure 1 shows the main progress of by Li and Allinsonrsquosbuilding recognition method [7] In Figure 1 Siagian andIttirsquos gist feature extraction method is used to extract thegist features from building images Then dimensionalityreduction algorithm is used to reduce the dimension of theoriginal feature vectors from 544 to a much lower dimensionbefore classification

3 Histogram of Oriented Gradient Based GistFeature (HOG-gist) Extraction

In this section we will introduce in detail our histogram oforiented gradient based gist feature (HOG-gist) extractionmethod and our building recognition method

31 Orientation Gist Feature Extraction The orientation gistfeatures can be extracted by the following five steps Thisprocess is shown by Figure 2

The process is as follows

(1) An image pyramid 119868(119888)119888 = 0 1 2 3 is created on theintensity channel 119868 (see (1)) with four spatial scalesranging from 1 1 (scale zero) to 1 8 (scale three) infour octaves A histogram of oriented gradient will becomputed in each scale of 119868(119888)

(2) Use gradient filter [minus1 0 1] with no smoothing [1415] to compute the horizontal 119866119909(119909 119910) and vertical119866119910(119909 119910) gradient of 119868(119888)

(3) Computemagnitude |119866(119909 119910)| and angle 120579(119909 119910) of thegradient [14 15]

1003816100381610038161003816119866 (119909 119910)1003816100381610038161003816 = radic119866119909 (119909 119910)2 + 119866119910 (119909 119910)2120579 (119909 119910) = arctan(119866119910 (119909 119910)119866119909 (119909 119910))

(5)

(4) Compute a histogram with 119887 orientation bins in0∘ndash180∘ Magnitude (|119866(119909 119910)|) whose angle 120579(119909 119910)belongs to the same bin will be added up as the valueof this bin The value of 119887 is determined according toexperimental results in Section 41

(5) Thehistograms can be normalized by1198712minus119867119910119904 (Lowe-style clipped L2 norm) [14] normalization method

After the computation of all the histograms of orientedgradient in four scales we can get four 119887-dimension vectors


A four spatial scalesrsquoimage pyramid I(c)

Compute magnitude and angle ofthe gradient in each scale of I(c)

Compute a histogram with b orientation bins in each scale of I(c)

0

01

02

03

0

01

02

03

1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 61 2 3 4 5 60

01

02

03

0

01

02

03

Figure 2 The process of the orientation gist feature extraction

which are the orientation gist feature vectors of HOG-gistnamely the orientation gist feature vectors of the HOG-gistare these four histograms of oriented gradient

32 Our Proposed Building Recognition Method Figure 3shows the building recognition method based on our HOG-gist The orientation channel in Figure 3 refers to the extrac-tion procession of orientation gist feature which has beenexplained in detail in Figure 2 and Section 31

In Figure 3 the color channel and intensity channelpresent the procession of extraction in color gist featurevector and intensity gist feature vector of the building ImageThe above extraction methods of gist feature vector are assame as the traditional method shown in Figure 1 Afterthe procession of the color channel and intensity channel 6intensity gist feature vectors and 12 color gist feature vectorshave been obtained from the intensity channel and the colorchannel respectively Then 6 intensity gist feature vectors 12color gist feature vectors and 4 orientation gist feature vectorswill be combined to our finally proposed HOG-gist Eachintensity gist feature vector and color gist feature vector areof 16-dimension vector In addition orientation gist featurevector is 119887 dimension Therefore HOG-gist equals a (288 +4 times 119887)-dimension (6 times 16 + 12 times 16 + 4 times 119887 = 288 + 4 times 119887) gistfeature vectorTheHOG-gist will reflect the characteristics ofthe original building image

Then dimensionality reduction algorithms (such as LPP[10] MFA [24] PCA [9] and NPE [25]) have been appliedto HOG-gist feature vector for the feature vector with lowerdimension Finally the feature vector of lower dimensionwill be classified via the classifiers such as Nearest Neighbor

Classifier (NN) [26] Support Vector Machine (SVM) [27]and BP-neural Network (BP) [28]

4 Experiments

To evaluate the performance of HOG-gist we carry outexperiments on the Sheffield Buildings Database [29] TheSheffield Buildings Database contains 3192 building imagesof 40 buildings and for each building the number of buildingimages varies from 100 to 400 The size of these images is160 times 120 Figure 4 shows sample images of the SheffieldBuildings Database From Figure 4 we can see that buildingsare taken fromdifferent viewpoints and images may be underdifferent scaling and illumination conditions and there areocclusion and rotation phenomena in some of the images

The number of building images of each building isdifferent so we select the first 20 images from each buildingand form a subset which we name as D1 D1 consists of 40buildings and 20 images for each building So D1 consists of800 buildings in total

In our experiments D1 was partitioned into differentsample collections We let 119866119898119875119899 indicate that for eachbuilding inD1119898 images were selected at random for trainingand the remaining 119899 (119899 = 20 minus 119898) images were employed fortesting For each 119866119898119875119899 50 random splits are generated andthe final result of this 119866119898119875119899 is obtained by taking the meanof the 50 recognition accuracy values

41 Experiments for Parameter Selection In this subsectionwe aim at choosing a proper parameter 119887 which is the num-ber of orientation bins of histogram of oriented gradient for


Linear filtering a four-scale pyramid Linear filtering a nine-scale pyramid

Orientation channel Color channel Intensity channel


Recognition result

Classifier



18 feature maps


18 gist feature vectors4b-dimension orientation gistfeature vectors

Com

pute

HO

G h

istog

ram

sat

4di

ffere

nt sc

ales

feature+(288 + 4 times b)-dimension gist

Figure 3 The building recognition method based on HOG-gist

(a)

(b)

(c)

Figure 4 (a)ndash(c) are sample images from categories 1 10 and 31 respectively

our HOG-gist We compute a histogram with 119887 orientationbins in the interval (0∘ndash180∘) If the step length of an angleis 2∘ 3∘ 4∘ 5∘ and 6∘ 119887 will be 90 (180∘2∘) 60 (180∘3∘)45 (180∘4∘) 36 (180∘5∘) and 30 (180∘6∘) respectively

Parameter selection experiments are conducted on 119866411987516 119866511987515 and 119866611987514 of the D1 subset respectively (Figure 5)In this experiment LPP [10] was used for dimensionalityreduction at the same time classification is conducted based


Number of bins

830

835

840

845

850Ac

cura

cy (

)

90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(a)Number of bins

860

865

870

875

Accu

racy

()

90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(b)

880

885

890

895

900

905

Accu

racy

()

Number of bins90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(c)

Figure 5 The mean recognition accuracy values of each value of parameter 119887 (a) 119866411987516 of D1 (b) 119866511987515 of D1 (c) 119866611987514 of D1

on the Nearest Neighbor Classifier (NN) [26] The meanrecognition rate corresponding to each value of 119887 is shownin Figures 4(a)ndash4(c)

From Figures 4(a)ndash4(c) it can be seen that HOG-gistachieves the highest recognition rate when the value of 119887 is60 As a result we set the value of 119887 to 60 in the followingexperiments Since there are 60 bins values in the histogramthe dimension of an orientation gist feature vector is 60There are four 60-dimension orientation gist feature vectorsextracted by the HOG-gist extraction methodTheHOG-gistis a 528-dimensional gist feature vector whose dimension issimilar to the dimension of Siagian and Ittirsquos gist

42 Building Recognition Using Different DimensionalityReduction Algorithms In this experiment we evaluated theperformance of our HOG-gist by comparing HOG-gist withSiagian and Ittirsquos gist LPP [10] NPE [25] PCA [9] and MFA[24] are employed as the dimensionality reduction algorithmrespectively Finally classification is conducted based on the

Nearest Neighbor Classifier (NN) [26] The mean accuracyvalues of Siagian and Ittirsquos gist and our HOG-gist are listed inline 1 and line 2 of each Table respectively

From the results shown in Tables 1ndash4 one can find thefollowing

(1) With the increasing number of training samplesthe mean recognition rates of the two gist featureextraction methods have risen differently

(2) Our HOG-gist shows a better performance thanSiagian and Ittirsquos gist regardless of which kind ofdimension reduction algorithm is selected

(3) In most situations feature dimensions of our HOG-gist corresponding to the best recognition results aremuch lower than those of Siagian and Ittirsquos gist Thisindicates that the texture and shape description abilityof our HOG-gist are better than those of Siagianand Ittirsquos gist Therefore our HOG-gist feature can


Table 1Mean recognition accuracyof Siagianamp Ittirsquos gist + LPP andHOG-gist + LPPThenumbers in parentheses indicate the correspondingfeature dimensions which give the best results after dimensionality reduction

119866311987517 119866411987516 119866511987515 119866611987514 119866711987513 119866811987512 119866911987511Siagian amp Ittirsquos gist + LPP 7699 (70) 7963 (80) 8125 (85) 8473 (90) 8655 (95) 8713 (97) 8927 (98)SM-gist + LPP 7982 (65) 8470 (69) 8732 (79) 8985 (64) 8984 (36) 9136 (57) 9165 (63)

Table 2 Mean recognition accuracy of Siagian amp Ittirsquos gist + NPE and HOG-gist + NPE The numbers in parentheses indicate thecorresponding feature dimensions which give the best results after dimensionality reduction

119866311987517 119866411987516 119866511987515 119866611987514 119866711987513 119866811987512 119866911987511Siagian amp Ittirsquos gist + NPE 7780 (58) 8053 (67) 8215 (75) 8598 (70) 8705 (85) 8895 (94) 9098 (96)SM-gist + NPE 8181 (50) 8576 (54) 8768 (59) 9052 (61) 9167 (63) 9176 (67) 9271 (70)

Table 3 Mean recognition accuracy of Siagian amp Ittirsquos gist + PCA and HOG-gist + PCA The numbers in parentheses indicate thecorresponding feature dimensions which give the best results after dimensionality reduction

119866311987517 119866411987516 119866511987515 119866611987514 119866711987513 119866811987512 119866911987511Siagian amp Ittirsquos gist + PCA 8182 (54) 8233 (46) 8310 (35) 8462 (54) 8525 (71) 8740 (77) 9017 (73)SM-gist + PCA 8196 (42) 8534 (29) 8748 (31) 8930 (62) 9003 (66) 9182 (69) 9228 (72)

Table 4 Mean recognition accuracy of Siagian amp Ittirsquos gist + MFA and HOG-gist + MFA The numbers in parentheses indicate thecorresponding feature dimensions which give the best results after dimensionality reduction

119866311987517 119866411987516 119866511987515 119866611987514 119866711987513 119866811987512 119866911987511Siagian amp Ittirsquos gist + MFA 8052 (47) 8450 (47) 8684 (62) 8883 (63) 9030 (76) 9119 (78) 9220 (79)SM-gist + MFA 8241 (48) 8629 (46) 8872 (41) 9125 (58) 9283 (64) 9365 (37) 9441 (66)

be reduced to a lower dimension Then the higherrecognition rate of HOG-gist is achieved

(4) MFA is a supervised subspace learning dimensionreduction algorithm The average recognition rate ofHOG-gist combined with MFA is higher than that ofHOG-gist combined with other dimension reductionalgorithms which is the same to Siagian and Ittirsquos gistfeature

(5) As an unsupervised dimension reduction algorithmthe performance of NPE is satisfied The mean recog-nition accuracy values of HOG-gist combined withNPE are only slightly lower than those of HOG-gistcombined with MFA

43 Building Recognition Using Different Classifiers Build-ing recognition was conducted by combining HOG-gistor traditional gist (Siagian and Ittirsquos gist) with differentclassifiers to compare the performances of HOG-gist andtraditional gist in this experiment LPP algorithm is thedimensionality reduction algorithm of HOG-gist And thenthe low-dimensional features after dimensionality reductionwere classified individually by using four different classifiersNearest Neighbor Classifier (NN) SVM with the radial basekernel function and BP-neural Network with two and threehidden layers The two BP-neural Networks are denotedas BP1 and BP2 in Figure 6 respectively Then the aboveexperiments were repeated for Siagian and Ittirsquos gist feature

The mean recognition results are in Figure 6 In Figure 6the solid line shows the result of HOG-gist combined witha certain classifier and the dashed line in the same coloris the result of Siagian and Ittirsquos gist combined with thesame classifier The horizontal axis of Figure 6 is the numberof training samples and the vertical axis represents themean recognition accuracy corresponding to each number oftraining samples

From Figure 4 we can make the following conclusions

(1) No matter which classifier combined with the HOG-gist it has gained higher mean recognition rate thanthe traditional gist (Siagian and Ittirsquos gist) combinedwith the same classifier which shows that the HOG-gist is superior to Siagian and Ittirsquos gist feature

(2) With SVM the HOG-gist has achieved the highestmean recognition rate the second highest recognitionrate iswithNN and the lowest recognition rate is withBP Siagian and Ittirsquos gist feature combined with theabove classifier also got the same result

(3) The mean recognition rate of Siagian and Ittirsquos gistwith SVM is higher than the recognition rate ofHOG-gist with BP or NN which shows that the selectionof classifier is as important as the selection of featureextraction method


60

65

70

75

80

85

90

95

Reco

gniti

on ac

cura

cy (

)

4 5 6 7 8 93

The number of training samples

HOG-gist + BP2Traditional gist + BP2HOG-gist + BP1Traditional gist + BP1

HOG-gist + SVMTraditional gist + SVMHOG-gist + NNTraditional gist + NN

Figure 6 The mean recognition accuracy of HOG-gist or tradi-tional gist (Siagian and Ittirsquos gist) combined with different classifierson the Sheffield Buildings Database

5 Conclusions

There are a lot of lines on the building surface so thetexture feature of buildings is more important than the colorfeature and intensity feature for building recognition tasks Inorder to improve the texture description ability of traditionalgist feature extraction method we proposed histogram oforiented gradient based gist (HOG-gist) feature extractionmethod Our method employs the normalized histograms oforiented gradients as orientation gist feature vectors of animageThese orientation gist vectors combined with intensityand color gist feature vectors extracted by the traditionalmethod are our HOG-gist The HOG-gist contains fourmultiorientation histograms (four orientation gist featurevectors) and its texture description ability is stronger thanthat of the traditional gist using Gabor filters with four anglesTheHOG-gist is a 528-dimensional gist feature vector whosedimension is similar to the dimension of Siagian and Ittirsquos gistbut its mean recognition accuracy is better than the meanrecognition accuracy of Siagian and Ittirsquos gist

Competing Interests

The authors declare that none of them have any competinginterests in themanuscript and there is no conflict of interestsregarding the publication of this article

Acknowledgments

This research is supported by (1) Doctoral Scientific Re-search Fund of Northeast Dianli University (BSJXM-201520)(2) Key Scientific and Technological Project of Scienceand Technology Department of Jilin Province of China(20150204007GX) and (3) National Natural Science Foun-dation of China (61602108)

References

[1] M M Ullah A Pronobis B Caputo J Luo P Jensfelt andH I Christensen ldquoTowards robust place recognition for robotlocalizationrdquo in Proceedings of the 2008 IEEE InternationalConference on Robotics and Automation (ICRA rsquo08) pp 530ndash537 IEEE Pasadena Calif USA May 2008

[2] M Belkin and P Niyogi ldquoLaplacian eigenmaps and spectraltechniques for embedding and clusteringrdquo Advances in NeuralInformation Processing Systems vol 14 no 6 pp 585ndash591 2002

[3] R Hutchings and W Mayol-Cuevas ldquoBuilding recognitionfor mobile devices incorporating positional information withvisual featuresrdquo Tech Rep CSTR-06-017 Computer ScienceUniversity of Bristol 2005

[4] Y Li and L G Shapiro ldquoConsistent line clusters for buildingrecognition in CBIRrdquo in Proceedings of the 16th InternationalConference on Pattern Recognition IEEE Computer SocietyQuebec City Canada August 2002

[5] W Zhang and J Kosecka ldquoHierarchical building recognitionrdquoImage and Vision Computing vol 25 no 5 pp 704ndash716 2007

[6] G Fritz C Seifert M Kumar and L Paletta ldquoBuilding detec-tion from mobile imagery using informative SIFT descriptorsrdquoin Image Analysis 14th Scandinavian Conference SCIA 2005Joensuu Finland June 19ndash22 2005 Proceedings vol 3540 ofLecture Notes in Computer Science pp 629ndash638 SpringerBerlin Germany 2005

[7] J Li and N M Allinson ldquoSubspace learning-based dimension-ality reduction in building recognitionrdquo Neurocomputing vol73 no 1ndash3 pp 324ndash330 2009

[8] C Siagian and L Itti ldquoRapid biologically-inspired scene clas-sification using features shared with visual attentionrdquo IEEETransactions on Pattern Analysis ampMachine Intelligence vol 29no 2 pp 300ndash312 2007

[9] I T Jolliffe Principal Component Analysis Springer Series inStatistics Springer Berlin Germany 2002

[10] X He ldquoLocality preserving projectionsrdquo Advances in NeuralInformation Processing Systems vol 45 no 1 pp 186ndash197 2010

[11] W S Rayens ldquoDiscriminant analysis and statistical patternrecognitionrdquo Journal of the Royal Statistical Society vol 168 no3 pp 635ndash636 2005

[12] B Li W Pang Y Liu et al ldquoBuilding recognition on sub-regionrsquos multiscale gist feature extraction and correspondingcolumns information based dimensionality reductionrdquo Journalof Applied Mathematics vol 2014 Article ID 898705 10 pages2014

[13] C Zhao C Liu and Z Lai ldquoMulti-scale gist feature manifoldfor building recognitionrdquo Neurocomputing vol 74 no 17 pp2929ndash2940 2011

[14] N Dalal and B Triggs ldquoHistograms of oriented gradients forhuman detectionrdquo in Proceedings of the 2005 IEEE ComputerSociety Conference on Computer Vision and Pattern Recognition(CVPR rsquo05) pp 886ndash893 IEEE San Diego Calif USA June2005

[15] O Deniz G Bueno J Salido and F De la Torre ldquoFacerecognition using histograms of oriented gradientsrdquo PatternRecognition Letters vol 32 no 12 pp 1598ndash1603 2011

[16] C-Y Su and J-F Yang ldquoHistogram of gradient phases a newlocal descriptor for face recognitionrdquo IET Computer Vision vol8 no 6 pp 556ndash567 2014

[17] S Ghafurian I Hacihaliloglu D N Metaxas V Tan and K Lildquo3D2D image registration using weighted histogram of gradi-ent directionsrdquo in Medical Imaging Image-Guided Procedures


Robotic Interventions and Modeling vol 9415 of Proceedings ofSPIE pp 1ndash7 2015

[18] P Torrione K D Morton R Sakaguchi and L M CollinsldquoHistogram of gradient features for buried threat detectionin ground penetrating radar datardquo in Proceedings of the 32ndIEEE International Geoscience and Remote Sensing Symposium(IGARSS rsquo12) pp 3182ndash3185 July 2012

[19] M Sharma and H Ghosh ldquoHistogram of gradient magnitudesa rotation invariant texture-descriptorrdquo in Proceedings of theIEEE International Conference on Image Processing (ICIP rsquo15)pp 4614ndash4618 Quebec City Canada September 2015

[20] M A Syarif Th S Ong and C Tee ldquoFingerprint recognitionbased on multi-resolution histogram of gradient descriptorsrdquoin The 8th International Conference on Robotic Vision SignalProcessing amp Power Applications Innovation Excellence TowardsHumanistic Technology vol 291 of Lecture Notes in ElectricalEngineering pp 189ndash196 Springer Berlin Germany 2014

[21] S Ahn J Park and J Chong ldquoBlurring image quality assess-ment method based on histogram of gradientrdquo in Proceedingsof the 19th Brazilian Symposium on Multimedia and the Web(WebMedia rsquo13) pp 181ndash184 Salvador Brazil November 2013

[22] A M Treisman and G Gelade ldquoA feature-integration theory ofattentionrdquo Cognitive Psychology vol 12 no 1 pp 97ndash136 1980

[23] L Itti C Koch and E Niebur ldquoAmodel of saliency-based visualattention for rapid scene analysisrdquo IEEE Transactions on PatternAnalysis and Machine Intelligence vol 20 no 11 pp 1254ndash12591998

[24] S YanD Xu B ZhangH-J ZhangQ Yang and S Lin ldquoGraphembedding and extensions a general framework for dimen-sionality reductionrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 29 no 1 pp 40ndash51 2007

[25] X He D Cai S Yan and H-J Zhang ldquoNeighborhood preserv-ing embeddingrdquo in Proceedings of the 10th IEEE InternationalConference on Computer Vision (ICCV rsquo05) pp 1208ndash1213 IEEEComputer Society Beijing China October 2005

[26] Y Freund ldquoExperiments with a new boosting algorithmrdquo inProceedings of the 30th International Conference on MachineLearning vol 13 pp 148ndash156 1996

[27] C Cortes and V Vapnik ldquoSupport-vector networksrdquo MachineLearning vol 20 no 3 pp 273ndash297 1995

[28] G E Hinton and T J Sejnowski ldquoLearning and relearningin Boltzmann machinesrdquo in Parallel Distributed ProcessingExplorations in the Microstructure of Cognition vol 1 pp 45ndash76 MIT Press Cambridge Mass USA 1986

[29] httpeeeproshefacukbuildingdatasetrar

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Human-ComputerInteraction

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filtering of the original image in orientation channels colorchannels and intensity channels in multiple spatial scalesEach feature map is divided into a 4 times 4 grid and the meanvalues of each grid were calculated to produce 16 values fora gist vector As a result the original image is representedby a 544-dimension feature vector To reduce computationalcosts and preserve discriminative information as much aspossible several manifold learning dimensionality reductionalgorithms such as principal component analysis (PCA)[9] locality preserving projections (LPP) [10] and lineardiscriminant analysis (LDA) [11] are used for dimensionalityreduction before recognition The gist feature extractionand dimensionality reduction-based building recognitionmethod proposed by Li and Allinson has been proven to bemore effective than those low-level feature methods [3ndash7] Liet al [12] proposed subregionrsquos multiscale gist feature (SM-gist) extraction method The SM-gist divided building imageinto 4times5 subregions and gist vectors are extracted from thesesubregions individually The interference of nonuniformillumination is mitigated by the SM-gist extraction methodZhao et al [13] proposed multiscale gist (MS-gist) featurefor building recognition The MS-gist features can be stableto capture the representation features of the building imageswith rotation variant lighting conditions and occlusions

The gist feature extraction methods proposed by Siagianand Itti were originally used for the task of scene recognitionbut building recognition is different from scene recognitionThis is because there are many lines on the building sur-face For building recognition tasks the texture feature ofbuildings is more important than the color and intensityfeatures Siagian and Ittirsquos gist feature extraction method usedGabor filters with only four angles to extract the orientationinformation So the texture description ability of Siagianand Ittirsquos gist feature extraction method is not good Toimprove the texture description ability of Siagian and Ittirsquosgist feature extraction method we propose histogram oforiented gradient based gist (HOG-gist) feature extractionmethod The histogram of oriented gradient (HOG) wasfirst proposed by Dalal and Triggs [14] Due to the strongtexture and shape description ability the HOG can be usedin human detection [14] face recognition [15 16] imageregistration [17] and many other tasks [18ndash21] Our pro-posed HOG-gist extraction method individually computesthe normalized histograms of multiorientation gradients forthe same image with four different scales These normalizedhistograms of oriented gradients are orientation gist featurevectors of an image These orientation gist vectors combinedwith intensity and color gist feature vectors proposed bythe traditional method are the proposed HOG-gist vec-tors

This paper is organized as follows we give a brieflyreview of Siagian and Ittirsquos gist feature extraction methodin Section 2 the histogram of oriented gradient basedgist (HOG-gist) feature extraction method is proposed inSection 3 recognition performance on the Sheffield BuildingsDatabase is detailed in Section 4 while Section 5 concludesthe paper

2 Gist Feature Extraction

In this section we give a brief review of Siagian and Ittirsquos gistfeature extraction method [8] and the building recognitionmethod proposed by Li and Allinson [7]

The psychological research [22] has proven that humancan grasp the ldquogistrdquo of an image by glancing at it for just afew seconds Siagian and Ittirsquos gist feature extraction methodis aiming to simulate this ability of human beings Siagianand Ittirsquos gist feature extraction method has two main stepssaliency feature map construction and gist feature extractionSaliency feature maps are constructed based on low-levelvisual features including the intensity channel color channeland orientation channel which are extracted in parallelEquation (1) is utilized to compute the intensity channel[8 23]

119868 = (119903 + 119892 + 119887) (1)

119877 119866 119861 119884 color channel [8 23] can be obtained by thefollowing equations

119877 = 119903 minus (119892 + 119887)2 119866 = 119892 minus (119903 + 119887)2 119861 = 119887 minus (119903 + 119892)2 119884 = (119903 + 119892)2 minus

1003816100381610038161003816119903 minus 11989210038161003816100381610038162 minus 119887

(2)

where 119903 119892 119887 represent the red green and blue channels of theRGB color space of the original image

For the intensity channel and the color channel fiveimage Gaussian pyramids 119868(120590) 119877(120590) 119866(120590) 119861(120590) and 119884(120590)with nine spatial scales ranging from 1 1 (scale zero) to1 256 (scale eight) in eight octaves are created [8 23] where120590 = 0 1 8 The intensity and color saliency feature mapscan be obtained by applying the center-surround operationto these Gaussian pyramids The center-surround operationdefined by Siagian and Itti is as follows [8 23] a pixel at scale119888 = 2 3 4 is the center and the corresponding pixels at scale119904 = 119888 + 120575 where 120575 = 3 4 is the surround From (3) we canget six intensity feature maps [8 23] and twelve color featuremaps [8 23] are obtained by (4)

119868 (119888 119904) = |119868 (119888) ⊝ 119868 (119904)| (3)

119877119866 (119888 119904) = |(119877 (119888) minus 119866 (119888)) ⊝ (119866 (119904) minus 119877 (119904))| 119861 (119888 119904) = |(119861 (119888) minus 119884 (119888)) ⊝ (119884 (119904) minus 119861 (119904))| (4)

where ⊝ denotes the cross-scale difference between twoimages in a Gaussian pyramid

Gabor filters with four different scales 119888 = 1 2 3 4 andfour orientations 120579 = 0∘ 45∘ 90∘ 135∘ are applied to theintensity channel 119868 and extract the 16 orientation featuremaps[8 23]




∘

90∘

135∘

4or

ient

atio

ns at

4 d

iffer

ent s

cale




34 feature maps

Recognition result

Classifier
















1003816100381610038161003816119866 (119909 119910)1003816100381610038161003816 = radic119866119909 (119909 119910)2 + 119866119910 (119909 119910)2120579 (119909 119910) = arctan(119866119910 (119909 119910)119866119909 (119909 119910))

(5)








0

01

02

03

0

01

02

03

1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 61 2 3 4 5 60

01

02

03

0

01

02

03







4 Experiments









Recognition result

Classifier



18 feature maps



Com

pute

HO

G h

istog

ram

sat

4di

ffere

nt sc

ales



(a)

(b)

(c)





Number of bins

830

835

840

845

850Ac

cura

cy (

)

90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(a)Number of bins

860

865

870

875

Accu

racy

()

90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(b)

880

885

890

895

900

905

Accu

racy

()

Number of bins90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(c)





























60

65

70

75

80

85

90

95

Reco

gniti

on ac

cura

cy (

)

4 5 6 7 8 93





5 Conclusions


Competing Interests


Acknowledgments


References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






∘

90∘

135∘

4or

ient

atio

ns at

4 d

iffer

ent s

cale




34 feature maps

Recognition result

Classifier
















1003816100381610038161003816119866 (119909 119910)1003816100381610038161003816 = radic119866119909 (119909 119910)2 + 119866119910 (119909 119910)2120579 (119909 119910) = arctan(119866119910 (119909 119910)119866119909 (119909 119910))

(5)








0

01

02

03

0

01

02

03

1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 61 2 3 4 5 60

01

02

03

0

01

02

03







4 Experiments









Recognition result

Classifier



18 feature maps



Com

pute

HO

G h

istog

ram

sat

4di

ffere

nt sc

ales



(a)

(b)

(c)





Number of bins

830

835

840

845

850Ac

cura

cy (

)

90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(a)Number of bins

860

865

870

875

Accu

racy

()

90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(b)

880

885

890

895

900

905

Accu

racy

()

Number of bins90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(c)





























60

65

70

75

80

85

90

95

Reco

gniti

on ac

cura

cy (

)

4 5 6 7 8 93





5 Conclusions


Competing Interests


Acknowledgments


References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in







0

01

02

03

0

01

02

03

1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 61 2 3 4 5 60

01

02

03

0

01

02

03







4 Experiments









Recognition result

Classifier



18 feature maps



Com

pute

HO

G h

istog

ram

sat

4di

ffere

nt sc

ales



(a)

(b)

(c)





Number of bins

830

835

840

845

850Ac

cura

cy (

)

90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(a)Number of bins

860

865

870

875

Accu

racy

()

90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(b)

880

885

890

895

900

905

Accu

racy

()

Number of bins90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(c)





























60

65

70

75

80

85

90

95

Reco

gniti

on ac

cura

cy (

)

4 5 6 7 8 93





5 Conclusions


Competing Interests


Acknowledgments


References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in







Recognition result

Classifier



18 feature maps



Com

pute

HO

G h

istog

ram

sat

4di

ffere

nt sc

ales



(a)

(b)

(c)





Number of bins

830

835

840

845

850Ac

cura

cy (

)

90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(a)Number of bins

860

865

870

875

Accu

racy

()

90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(b)

880

885

890

895

900

905

Accu

racy

()

Number of bins90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(c)





























60

65

70

75

80

85

90

95

Reco

gniti

on ac

cura

cy (

)

4 5 6 7 8 93





5 Conclusions


Competing Interests


Acknowledgments


References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




Number of bins

830

835

840

845

850Ac

cura

cy (

)

90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(a)Number of bins

860

865

870

875

Accu

racy

()

90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(b)

880

885

890

895

900

905

Accu

racy

()

Number of bins90 (2∘) 60 (3∘) 45 (4∘) 36 (5∘) 30 (6∘)

(c)





























60

65

70

75

80

85

90

95

Reco

gniti

on ac

cura

cy (

)

4 5 6 7 8 93





5 Conclusions


Competing Interests


Acknowledgments


References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






















60

65

70

75

80

85

90

95

Reco

gniti

on ac

cura

cy (

)

4 5 6 7 8 93





5 Conclusions


Competing Interests


Acknowledgments


References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in
























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



research article histogram of oriented gradient based gist

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