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FRIP: A Region-Based Image Retrieval Tool Using Automatic Image Segmentation and Stepwise Boolean AND Matching
IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 7, NO. 1, FEBRUARY 2005, pp. 105-113ByoungChul Ko and Hyeran ByunReporter: Jen-Bang Feng
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Outline
Image Retrieval Content-Based Image Retrieval The Proposed Scheme Experimental Results
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Image Retrieval
ImageDB
Image retrievalscheme FeaturesFeaturesFeaturesFeaturesFeaturesFeaturesFeatures
QueryImage
Image retrievalscheme
Feature
Compare SearchingResults
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Content-Based Image Retrieval From text-based retrieval scheme
WWW search engine Query-by-image in early 90’s From global to local (region)
Region Of Interest
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The Proposed Scheme
1. Image Segmentation Two-Level Segmentation Using Adaptive Circular Filter a
nd Bayes’ Theorem Iterative Level Using Region Labeling and Iterative Regio
n Merging2. Feature extraction
Color Texture Normalized Area Shape and Location
3. Stepwise Similarity Matching
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Two-Level Segmentation Using Adaptive Circular Filter and Bayes’ Theorem
Adaptive Circular Filter
Image(RGB)
Image(CIE Lab)
SmoothedImage(CIE Lab)
Remove middle frequency
Color histogram
Separate regions by circular filters
RegionsRegionsRegionsRegions
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Two-Level Segmentation Using Adaptive Circular Filter and Bayes’ Theorem
a is similar to c in colorbut a is closer to b than c Example of circular filtering process
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Two-Level Segmentation Using Adaptive Circular Filter and Bayes’ Theorem
xyyx
CyxyxM
MyxMMyxM
MyxMyxM
cc
MccCP
CcPCPCcPCP
CcPCPcCP
,
,,
,,
,,
else
then ,5.0| if
||
||
Three circular filters3x3, 7x7, 11x11
CM: the most frequently observed histogram binsCM: other binscx,y: center value of CM
MC: the major class color
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Two-Level Segmentation Using Adaptive Circular Filter and Bayes’ Theorem
division according to the edge distribution Selected filter, 3x3, 7x7, 11x11
Segmentation result Final segmented image
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Iterative Level Using Region Labeling and Iterative Region Merging
Image(RGB)
Image(CIE Lab)
SmoothedImage(CIE Lab)
Remove middle frequency
Color histogram
Separate regions by circular filters
RegionsRegionsRegionsRegions
RegionsRegions
Merge regions
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Iterative Level Using Region Labeling and Iterative Region Merging
N
i
imbb
imaa
imLL TRRRRRRIf
For the N neighbor regions
Then merge the regions
If the number of regions is larger than 30Then increase the threshold and repeat the circular filter
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Feature extraction Color
Average AL, Aa, Ab
Variance VL, Va, Vb
Color distance of Q and T
2,,,,
,
VbVaVLCCC
AbAaAlCCC
CTQ TQTQd
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Feature extraction Texture
Biorthogonal wavelet frame (BWF) The X-Y directional amplitude Xd, Yd
The distance in texture
T
T
Q
QTTQ Xd
Yd
Xd
Ydd ,
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Feature extraction Normalized Area
NPQ =
(Size of the region) / (Size of the image)
TQNArea
TQ NPNPd ,
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Feature extraction Shape and Location
The global geometric shape feature eccentricity
Estimate the bounding rectangle for each segmented region
For the major axis Rmax and minor axis Rmin
max
min
max
min
T
T
Q
Q
R
R
R
RE
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Feature extraction Shape and Location
The local geometric shape feature MRS (modified radius-based shape signature)
invariant under shape’s scaling, rotation, and translation
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Feature extraction Shape and Location
The local geometric shape feature MRS (modified radius-based shape signature)
Extracts 12 radius distance values
2
1 1
,
11
,min
N
i i
iN
j j
jC
ckwisecountercloclockwiseMRS
TQ
T
Q
T
Q
NNd
ddd
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Stepwise Similarity Matching
r
i
p
j
ij
ijjj tqDwYXSim
1 1
,,
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Experimental Results
query: flower best case
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Experimental Results
query: shipworst case
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