International Journal of Computer Science and Applicationsc© Technomathematics Research Foundation

Vol. 8 No. 1, pp. 36 - 53, 2011

Quantitative Evaluation of Color Image Segmentation Algorithms

ANDREEA IANCU

Software Engineering Department, University of Craiova, Bd. Decebal 107

Craiova, Romania

andreea.iancu@itsix.com

BOGDAN POPESCU

Software Engineering Department, University of Craiova, Bd. Decebal 107

Craiova, Romaniabogdan.popescu@itsix.com

MARIUS BREZOVAN

Software Engineering Department, University of Craiova, Bd. Decebal 107

Craiova, Romania

brezovan marius@software.ucv.ro

EUGEN GANEA

Software Engineering Department, University of Craiova, Bd. Decebal 107

Craiova, Romaniaganea eugen@software.ucv.ro

The present paper addresses the nowadays field of image segmentation by offering an

evaluation of several existing approaches. The paper offers a comparison of the experi-mental results from the error measurement point of view.

We introduce a new method of salient object recognition that takes into considerationcolor and geometric features in order to offer a conclusive result. Our new segmentation

method introduced in the paper has revealed very good results in terms of comparison

with the already known object detection methods. We use a set of error measures toanalyze the consistency of different segmentations provided by several well known algo-

rithms. The experimental results offer a complete basis for parallel analysis with respect

to the precision of our algorithm, rather than the individual efficiency.

Keywords: color segmentation; graph-based segmentation; contour-based evaluationmethods.

1. Introduction

Segmentation of color images is one of the most important subjects when it comes

to image processing. There have been several studies achieved so far with good

results in terms of relevant results, but it is still an open topic for computer vision

field. The evaluation of image segmentation [Martin (2001)] focuses on two main

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properties: objectivity and generality. By objectivity we understand that all the test

images in the benchmark should have an unambiguous ground-truth segmentation

so that the evaluation can be conducted objectively. On the other hand, generality

refers to the fact that the test images in the benchmark should have a large variety

so that the evaluation results can be extended to other images and applications.

Quantification of the performance on a segmentation algorithm remains a chal-

lenging task. This is largely due to image segmentation being a non-strict-defined

problem; there is no single ground truth segmentation against which the output of

an algorithm may be compared. Rather the comparison is to be made with the set

of all possible perceptually consistent interpretations of the image, of which only a

small fraction is usually available.

The problem of segmentation is an important research field and many segmenta-

tion methods have been proposed in the literature so far ([Fu (1981)],[Felzenszwalb

(2004)],[Comaniciu (2002)],[Shi (2000)]). The target of image segmentation is the

domain-independent partition of the image into a set of regions which are visually

distinct and uniform with respect to some property, such as grey level, texture or

color.

One of the main characteristics of the studied approaches is the time of fusion

which can be either embedded in the detection of regions or placed at the end of

both processes [Falah (1994)].

Embedded integration is related to the integration through the definition of

new parameters or a new decision criterion for the segmentation operation. Most

commonly, the edge information is first extracted and is then used within the seg-

mentation algorithm which is based on regions. As an example, the edge information

can be used to define the seed points from which regions are grown. The purpose of

this integration strategy is to use boundary information to avoid some of the issues

of region-based techniques.

Post-processing integration is performed after the image has been processed

using the two different methods (boundary-based and region-based techniques).

Contour and region information are extracted in the first step. After that a fusion

process tries to exploit the dual information in order to modify, or refine, the initial

segmentation obtained by a single technique. The goal is the improvement of the

initial results and a more accurate segmentation.[Freixenet (1997)]

The main objective of this paper is to emphasize the very good results of image

segmentation obtained by our segmentation technique, Graph Based Salient Object

Detection, and to compare them with other existing methods. The algorithms that

we use for comparison are: Normalized Cuts, Efficient Graph-Based Image Segmen-

tation (Local Variation) and Mean Shift. All of them are complex and well known

algorithms, with very good results in this area and building the knowledge based

on their results represents a solid reference.

The experiments were completed using the images and ground-truth segmen-

tations in the Berkeley segmentation data set [Berkeley (2003)]. The dataset has

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proved quite useful for our work in order to evaluate the effectiveness of different

edge filters as indicators of boundaries. Since the ground-truth segmentation may

not be well and uniquely defined, each test image in the Berkeley benchmark is

manually segmented by a group of people. Being various and extensive, we expect

that our results will find further use based on the mechanism for computing the

consistency of different segmentations.

The segmentation accuracy is measured taking into consideration the global con-

sistency error and the local consistency error. We will provide comparative results

that reflect a well-balanced behavior of the algorithm we propose.

The paper is organized as follows. In Section 3 we briefly present previous studies

in the domain of image segmentation and the segmentation method we propose. The

performance evaluation methodology is presented in Section 4. The experiments and

their results are presented in Section 5. Section 6 concludes the paper and outlines

the main directions of the future work.

2. Related Work

Evaluation of image segmentation is an open subject in today’s image processing

field. The goal of existing studies is to establish the accuracy of each individual

approach and find new improvement methods.

Some of previous works in this domain do not require ground-truth image seg-

mentation as the reference. In these methods, the segmentation performance is

usually measured by some contextual and perceptual properties, such as the ho-

mogeneity within the resulting segments and the inhomogeneity across neighboring

segments.

Most of segmentation methods require ground truth image segmentations as

reference.

Since the construction of the ground-truth segmentation for many real images

is labor-intensive and sometimes not well or uniquely defined, most prior image

segmentation methods are only tested on some special classes of images used in

special applications where the ground-truth segmentations are uniquely defined,

synthetic images where ground-truth segmentation is also well defined, and/or a

small set of real images.

The main drawback of providing such reference is represented by the resources

that are needed. However, after analyzing the differences between the image under

study and the ground truth segmentation, a performance proof is obtained.

Region-based segmentation methods can be broadly classified as either model-

based [Carson (2002)] or visual feature-based [Fauqueur (2004)] approaches. A dis-

tinct category of region-based segmentation methods that is relevant to our ap-

proach is represented by graph-based segmentation methods. Most graph-based

segmentation methods attempt to search a certain structures in the associated edge

weighted graph constructed on the image pixels, such as minimum spanning tree

[Felzenszwalb (2004)], or minimum cut [Shi (2000)].

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Related work on quantitative segmentation evaluation includes both standalone

evaluation methods, which do not make use of a reference segmentation, and relative

evaluation methods employing ground truth.

For standalone evaluation of image segmentations metrics for intra-object ho-

mogeneity and inter-object disparity have been proposed in [Levine (1985)]. A com-

bined segmentation and evaluation scheme is developed in [Zhang (2002)]. Although

standalone evaluation methods can be very useful in such applications, their results

do not necessarily coincide with the human perception of the goodness of segmen-

tation. However, when a reference mask is available or can be generated, relative

evaluation methods are preferred in order the segmentation results to coincide with

the human perception of the goodness of segmentation.

Berkeley image segmentation benchmark is the reference that we use for our

study. Using the same input, the provided image dataset, we are developing a cus-

tomized methodology in order to efficiently evaluate our algorithm.

Martin et al. [Martin (2001)] propose two metrics that can be used to evaluate

the consistency of a pair of segmentations based on human segmentations. The mea-

sures are designed to be tolerant to refinement. A methodology for evaluating the

performance of boundary detection techniques with a database containing ground-

truth data was developed in [Martin (2004)]. It is based in the comparison of the

detected boundaries with respect to human-marked boundaries using the Precision-

Recall framework. Another evaluation are based on pixel discrepancy measures. In

[Huang (1995)], the use of binary edge masks and scalable discrepancy measures

is proposed. Odet et al. [Odet (2002)] propose also an evaluation method based

on edge pixel discrepancy, but the establishment of a correspondence of regions

between the reference mask and the examined one is considered.

The closest work to ours is [Felzenszwalb (2004)], in which an image segmenta-

tion is produced by creating a forest of minimum spanning trees of the connected

components of the associated weighted graph of the image. The novelty of our con-

tribution concerns two main aspects: (a) in order to minimize the running time we

construct a hexagonal structure based on the image pixels, that is used in both

color-based and syntactic-based segmentation algorithms, and (b) we propose an

efficient method for segmentation of color images based on spanning trees and both

color and syntactic features of regions.

3. Segmentation Methods

We will compare four different segmentation techniques, the Mean Shift-Based seg-

mentation algorithm [Comaniciu (2002)], Efficient Graph-Based segmentation algo-

rithm [Felzenszwalb (2004)], Normalized Cuts segmentation algorithm [Shi (2000)]

and our own region-based segmentation method. We have chosen Mean Shift-Based

segmentation because it is generally effective and has become widely-used in the

vision community. The Efficient Graph-Based segmentation algorithm was chosen

as an interesting comparison to the Mean Shift. Its general approach is similar,

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however, it excludes the mean shift filtering step itself, thus partially addressing

the question of whether the filtering step is useful. Due to its computational effi-

ciency, Normalized Cuts represents a solid reference in our study. We use all these

algorithms as terms of comparison for the evaluation we performed.

3.1. Graph-Based Salient Object Detection

We present an efficient segmentation method that uses color and some geometric

features of an image to process it and create a reliable result [Burdescu (2009)].

The used color space is RGB because of the color consistency and its computational

efficiency.

Fig. 1. The grid-graph constructed on the hexagonal structure of an image

What is particular at this approach is the basic usage of hexagonal structure

instead of color pixels. In this way we can represent the structure as a grid-graph

G = (V,E) where each hexagon h in the structure has a corresponding vertex v ∈ V ,

as presented in Figure 1. Each hexagon has six neighbors and each neighborhood

connection is represented by an edge in the set E of the graph. For each hexagon

on the structure two important attributes are associated: the dominant color and

the coordinates of the gravity center. Basically, each hexagonal cell contains eight

pixels: six from the frontier and two from the middle.

Image segmentation is realized in two distinct steps. The first step represents a

pre-segmentation step when only color information is used to determine an initial

segmentation. The second step represents a syntactic-based segmentation step when

both color and geometric properties of regions are used.

The first step of the segmentation algorithm uses a color-based region model

and will produce a forest of maximum spanning trees based on a modified form

of the Kruskal’s algorithm. In this case the evidence for a boundary between two

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adjacent regions is based on the difference between the internal contrast and the ex-

ternal contrast between the regions. The color-based segmentation algorithm builds

a maximal spanning tree for each salient region of the input image.

The second step of the segmentation algorithm uses a new graph, which has

a vertex for each connected component determined by the color-based segmenta-

tion algorithm. In this case the region model contains in addition some geometric

properties of regions such as the area of the region and the region boundary. The

final segmentation step produces a forest of minimum spanning trees based on a

modified form of the Bor̊uvka’s algorithm. Each determined minimum spanning tree

represents a final salient region determined by the segmentation algorithm.

3.2. Efficient Graph-Based Image Segmentation

Efficient Graph-Based image segmentation [Felzenszwalb (2004)], is an efficient

method of performing image segmentation. The basic principle is to directly process

the data points of the image, using a variation of single linkage clustering without

any additional filtering. A minimum spanning tree of the data points is used to per-

form traditional single linkage clustering from which any edges with length greater

than a given threshold are removed [Unnikrishnan (2007)].

Let G = (V,E) be a fully connected graph, with m edges {ei} and n vertices.

Each vertex is a pixel, x, represented in the feature space. The final segmenta-

tion will be S = (C1, ..., Cr), where Ci is a cluster of data points. The algorithm

[Felzenszwalb (2004)] can be shortly presented as follows:

(1) Sort E = (e1, ..., em) such that |et| ≤ |e′t|∀t < t′

(2) Let S0=({x1}, ..., {xn}) in other words each initial cluster contains exactly one

vertex.

(3) For t = 1, ...,m

(a) Let xi and xj be the vertices connected by et.

(b) Let Ct−1xi

be the connected component containing point xi on iteration t− 1

and li = maxmstCt−1xi

be the longest edge in the minimum spanning tree of

Ct−1xi

. Likewise for lj .

(c) Merge Ct−1xi

and Ct−1xj

if:

|et| < min

{li +

k

Ct−1xi

, lj +k

Ct−1xj

}(1)

(4) S = Sm.

3.3. Normalized Cuts

Normalized Cuts method models an image using a graph G = (V,E), where V is

a set of vertices corresponding to image pixels and E is a set of edges connecting

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neighboring pixels. The edge weight w(u, v) describes the affinity between two ver-

tices u and v based on different metrics like proximity and intensity similarity. This

weight can be particulary defined for each case, depending on the nature of the ex-

periment and the area of interest. For example, we can take into consideration the

Euclidean distance between two nodes or we can combine the brightness value of a

pixel with its spatial location and define a complex weight formula. The algorithm

segments an image into two segments that correspond to a graph cut (A,B), where

A and B are the vertices in the two resulting subgraphs.

The segmentation cost is defined by:

Ncut(A,B) =cut(A,B)

assoc(A, V )+

cut(A,B)

assoc(B, V )(2)

where cut(A,B) =∑

u∈A,v∈B w(u, v) is the cut cost of (A,B) and assoc(A, V ) =∑u∈A,v∈V w(u, v) is the association between A and V . The algorithm finds a graph

cut (A,B) with a minimum cost in Eq.(1). Since this is a NP-complete problem, a

spectral graph algorithm was developed to find an approximate solution [Shi (2000)].

This algorithm can be recursively applied on the resulting subgraphs to get more

segments. For this method, the most important parameter is the number of regions

to be segmented. Normalized Cuts is an unbiased measure of dissociation between

the subgraphs, and it has the property that minimizing normalized cuts leads di-

rectly to maximizing the normalized association relative to the total association

within the sub-groups.

3.4. Mean Shift

The Mean Shift-Based segmentation technique [Comaniciu (2002)] is one of many

techniques dealing with “feature space analysis”. Advantages of feature-space meth-

ods are the global representation of the original data and the excellent tolerance to

noise [Duda (2000)]. The algorithm has two important steps: a mean shift filtering

of the image data in feature space and a clustering process of the data points already

filtered. During the filtering step, segments are processed using the kernel density

estimation of the gradient. Details can be found in[Comaniciu (2002)]. A uniform

kernel for gradient estimation with radius vector h = [hs, hs, hr, hr, hr] is used. hs

is the radius of the spatial dimensions and hr the radius of the color dimensions.

Combining these two parameters, complex analysis can be performed while training

on different subjects.

Mean shift filtering is only a preprocessing step. Another step is required in the

segmentation process: clustering of the filtered data points {x′}. During filtering,

each data point in the feature space is replaced by its corresponding mode. This

suggests a single linkage clustering that converts the filtered points into a segmen-

tation.

Another paper that describes the clustering is [Christoudias (2002)]. A region

adjacency graph (RAG) is created to hierarchically cluster the modes. Also, edge

information from an edge detector is combined with the color information to better

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guide the clustering. This is the method used in the available EDISON system, also

described in [Christoudias (2002)]. The EDISON system is the implementation we

use in our evaluation system.

4. Region-based performance evaluation

We present comparative results of segmentation performance for our region based

segmentation method and the three alternative segmentation methods mentioned

above.

Our evaluation measure is mainly related to the consistency between segmen-

tations. We use segmentation error measures that provide an objective analysis of

the segmentation algorithms.

The region-based scheme is intended to evaluate segmentation quality in terms

of the precision of the extracted region boundaries.

We referred a set of metrics in order to provide a relevant comparison between

the segmentation methods and the human segmentation. The two error measures are

GCE (Global Consistency Error) and LCE (Local Consistency Error). The Global

Consistency Error assumes that one of the segmentations must be a refinement of

the other, and forces all local refinements to respect the same criteria. The Local

Consistency Error allows for refinements to occur in both ways at different locations

in the segmentation (namely from one segmentation to another and viceversa).

We applied the measures to the human segmentation from Berkeley segmenta-

tion dataset and the segmentation results we obtained applying the four selected

algorithms.

A potential problem for a measure of consistency between segmentations is that

there is no unique human segmentation of an image, since each human perceives

the scene differently. In this situation you could declare the segmentations incon-

sistent. However, if one segmentation is a refinement of the other, then the error

should be small. Therefore, the measures are designed to be tolerant to refinement.

Some other aspects to be taken into account are that error measure should not

depend on the pixelation level and should be tolerant to noise along region bound-

aries.[Unnikrishnan (2000)]

We will evaluate the performance of our algorithm on the Berkeley Segmen-

tation Database (BSD) [Berkeley (2003)]. We will refer the characteristics of the

error metrics previously defined by Martin et al. [Martin (2001)], explore potential

problems with these metrics in order to evaluate the quality of each segmentation

and to characterize its performance over a range of parameter values.

The current public version of the Berkeley Segmentation Database is composed

of 300 color images. The images have a size of 481 × 321 pixels, and are divided

into two sets, a training set containing 200 images that can be used to tune the

parameters of a segmentation algorithm, and a testing set containing the remaining

100 images on which the final performance evaluations should be carried out.

We built a custom benchmark framework, that processes the Berkeley dataset,

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converts it to our proprietary format and preforms parallel analysis. Additionally, we

adapted the other mentioned algorithms to the same evaluation format for unitary

purposes.

The human segmented images provide the ground truth boundaries. Therefore,

any boundary marked by a human subject is considered to be valid. Since there are

multiple segmentations of each image by different subjects, it is the collection of

these human-marked boundaries that constitutes the ground truth. Based on the

output of the previously presented algorithms for a set of images, we will determine

how well the ground truth boundaries are approximated.

In order to determine an algorithm’s efficiency by comparing it to the ground

truth boundaries, a threshold of the boundary map is needed.

We are providing an additional evaluation based on histogram representation of

the error density characteristic for each algorithm.

5. Experimental Results

Our study of segmentation quality is based on experimental results and uses the

Berkeley segmentation dataset provided at [Berkeley (2003)].

5.1. GCE and LCE Metrics

We used two metrics in order to provide an objective comparison between the four

segmentation methods and the human segmentation. The two error measures are

described below. We applied the measures to the Berkeley segmentation database

and the segmentation results of the four algorithms.

In order to describe the segmentation errors, we considered two different seg-

mentations S1 and S2 and calculated a value in the range [0..1] where 0 represents

no error. For a given pi we considered segments S1 and S2 that contain the pixel.

If one segment is a proper subset of the other, then the pixel lies in an area of

refinement, and the local error should be zero. Otherwise, the two regions overlap

in a inconsistent manner and we should calculate the corresponding error. We use

\ to denote the set difference and |x| for cardinality of set x.If R(S, pi) is the set

of pixels corresponding to the region in segmentation S that contains pixel pi, the

local refinement error is defined as:

E(S1, S2, pi) =|R(S1, pi)\R(S2, pi)|

|R(S1, pi)|(3)

This local error measure is not symmetric. It encodes a measure of refinement

in one direction only: E(S1, S2, pi) is zero precisely when S1 is a refinement of S2 at

pixel pi, but not vice versa. Considering this local refinement error in each direction

at each pixel, there are two methods to combine the values into an error measure

for the entire image. We apply two error measures as follows: Global Consistency

Error (GCE) that forces all local refinements to be in the same direction and Local

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Consistency Error (LCE) that allows refinement in different directions in different

parts of the image.[Martin (2001)]

For a given n as the number of pixels we have:

GCE(S1, S2) =1

nmin

{∑i

E(S1, S2, pi),∑i

E(S2, S1, pi)

}(4)

LCE(S1, S2) =1

n

∑i

min{E(S1, S2, pi), E(S2, S1, pi)} (5)

In order to proper evaluate the segmentation method we propose, we first need to

better understand how the GCE and LCE error metrics work. Given two extreme

cases:an under-segmented image, where every pixel has the same label (i.e. the

segmentation contains only one region spanning the whole image), and a completely

over-segmented image in which every pixel has a different label.

From the definitions of the GCE and LCE we can see that both measures eval-

uate to 0 on both of these extreme situations regardless of what segmentation they

are being compared to. The reason for this can be found in the tolerance of these

measures to refinement. Any segmentations is a refinement of the completely under-

segmented image, while the completely over-segmented image is a refinement of any

other segmentation.

In order to have a better analysis result and a more complete description for

the errors we considered, we have performed 10 different tests for each subject per

algorithm - Fig. 2.

Fig. 2. Average GCE vs. LCE for Berkeley test images

More precisely, by varying several key parameters, we have obtained 10 dis-

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tinct points that define the errors for each approach. For Normalized Cuts

[Shi (2000)] we have modified the number of segments in the range of

{5, 10, 12, 15, 20, 25, 30, 40, 50, 70}. The variable parameter for Efficient Graph−Based Image Segmentation [Felzenszwalb (2004)] was the scale of observation, k

, in range {100, 200, 300, 400, 500, 600, 700, 800, 900, 1000}. For Mean-Shift [Co-

maniciu (2002)] we have made 10 combinations from Spatial Bandwidth {8, 16}and Range Bandwidth {4, 7, 10, 13, 16}.

We calculated the GCE and LCE average values for the 100 test images pro-

vided by Berkeley. Figure 2 illustrates the GCE vs. LCE graphic representation.

In the resulting diagram (Fig. 2) we can see that the GCE vs. LCE error metric

for our proposed method, denoted GBSOD - Graph Based Salient Object Detection

is situated below the values for the other algorithms indicating a better performance

result, a smaller average error and a balanced algorithm. Analyzing the set of re-

sults for each parameter per algorithm, it’s easy to distinguish which algorithm is

generating better results; the smaller the error it is, the better is the accuracy of

the respective algorithm.

5.2. Histogram based evaluation

We elaborated a histogram-based evaluation mechanism aimed to compare the seg-

mentation results for the studied algorithms via the errors metrics.

In order to achieve this, we considered the human segmentation as the ground-

truth segmentation and compared each algorithm with it, measuring the error met-

rics GCE and LCE.

For each algorithm we analyzed the 100 test images from Berkley and calculated

the corresponding GCE and LCE. The histograms presented below illustrate this

approach (Fig.3 - Fig. 10).

For a better description of the histogram based analysis, in Fig.3 and Fig.4 we

have depicted the distribution of the the values of GCE respectively LCE for the

100 images processed using GBSOD-Graph Based Salient Object Detection algo-

rithm. It is very important that these value are more concentrated on smaller error

values, which gives us the confidence that the presented method has good results.

Fig.3 and Fig.4 show that both GCE and LCE values are concentrated on small

values (between 0.3 and 0.4). Both errors are taking values between 0 and 1, where

0 is no error case and 1 is worse case, thus in our results the errors are taking small

values, closer to 0.

In Fig.5 and Fig.6 it can be seen the same analysis for Normalized Cuts, in Fig.7

and Fig.8 for Efficient Graph-Based Image Segmentation and in Fig.9 and Fig.10

for Mean-Shift algorithm.

Taking into consideration the interval where GCE and LCE are taking values

(0,1), we can make an easy comparison and notice that N−Cuts is giving the worse

results of all the algorithms we’ve selected.

The segmentation accuracy provides an upper bound of the segmentation perfor-

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Fig. 3. GCE for Graph Based Salient Object Detection

Fig. 4. LCE for Graph Based Salient Object Detection

mance by assuming an ideal postprocess of region merging for applications without

a priori known exact ground truth. For the extreme case where each pixel is parti-

tioned as a segment, the upper-bound performance obtained is a meaningless value

of 100%. This is similar to the GCE and LCE measures developed in the Berkeley

benchmark. But the difference is that GCE and LCE also result in meaningless

high accuracy when too few segments are produced, such as the case where the

whole image is partitioned as a single segment. In this paper, we always set the

segmentation parameters to produce a reasonably small number of segments when

applying the strategy to merge the image regions.

Figures Fig.11 and Fig.12 are illustrating a comparison between all the four

presented algorithms; it gives a good perspective on the what error values generates

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Fig. 5. GCE for Normalized Cuts

Fig. 6. LCE for Normalized Cuts

each studied algorithm.

Figures Fig. 13, 14, 15 are showing several comparisons between the different

segmentation methods we have studied and presented in this paper.

6. Conclusion

In this paper we described a novel graph-based algorithm for image segmentation

and extraction of visual objects. We’ve intended to perform a complex evaluation

experiment starting from several strong segmentation strategies and some evalua-

tion metrics.

Our segmentation method and other three segmentation methodologies were

14

Fig. 7. GCE for Efficient Graph-Based Image Segmentation

Fig. 8. LCE for Efficient Graph-Based Image Segmentation

chosen for the experiment, and the complementary nature of the methods was

demonstrated in the results. The study results offer a clear view of the effectiveness

of each segmentation algorithm, trying in this way to offer a solid reference for

future studies.

The images presented above offer a good evidence that our own segmentation

method performs very well on a variety of images from different domains. There has

been recent progress in developing quantitative measures of segmentation quality

that can be used to evaluate and compare different segmentation algorithms. More

than that, considerable progress has been made on studying and implementing

segmentation methods.

Future work will be carried out in the direction of extending the evaluation

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Fig. 9. GCE for Mean-Shift

Fig. 10. LCE for Mean-Shift

mechanism, improving the evaluation metrics and enlarging the reference segmen-

tation methods. We will also continue to work on integration of syntactic visual

information into a semantic level of a semantic image processing and indexing sys-

tem.

Acknowledgment

This work has been supported by CNCSIS−UEFISCSU , project number PNII−IDEI code/2008.

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Fig. 11. GCE overall comparison

Fig. 12. LCE overall comparison

Fig. 13. Comparative segmentation results A : Human Segmentation, Graph-based Salient ObjectDetection, Normalized Cuts, Efficient Graph-Based, Mean-Shift

References

Berkeley Segmentation and Boundary Detection Benchmark and Dataset, 2003,

17

Fig. 14. Comparative segmentation results B: Human Segmentation, Graph-based Salient ObjectDetection, Normalized Cuts, Efficient Graph-Based, Mean-Shift

Fig. 15. Comparative segmentation results C: Human Segmentation, Graph-based Salient ObjectDetection, Normalized Cuts, Efficient Graph-Based, Mean-Shift

http://www.cs.berkeley.edu/projects/vision/grouping/segbench.Burdescu, D., Brezovan, M., Ganea, E. and Stanescu, L. A New Method for Segmentation

of Images Represented in a HSV Color Space, Lecture Notes in Computer Science,5807, 606-617, 2009.

Carson, C., Belongie, S., Greenspan, H. and Malik, J. Blobworld: Image segmentation us-ing expectation-maximization and its application to image querying and classification,IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(8),1026–1037, 2002.

Christoudias, C., Georgescu, B. and Meer, P. Synergism in Low Level Vision, Proc. IntlConf. Pattern Recognition, vol. 4, pp. 150-156, 2002.

Comaniciu, D. and Meer, P.Mean Shift: A Robust Approach toward Feature Space Analysis,IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, pp. 603-619, 2002.

Duda, R.O., Hart, P.E. and Stork, D.G. Pattern Classification, John Wiley & Sons, NewYork, 2000.

Falah, R., Bolon, P., Cocquerez, J.A region-region and region-edge cooperative approach ofimage segmentation. International Conference on Image Processing. Volume 3., Austin,Texas 470474, 1994.

Fauqueur, J. and Boujemaa, N. Region-based image retrieval: Fast coarse segmentationand fine color description, Journal of Visual Languages and Computing, 15(1), 69–95,2004.

Felzenszwalb, P. and Huttenlocher, D. Efficient Graph-Based Image Segmentation, Intl J.Computer Vision, vol. 59, no. 2, 2004.

18

Freixenet, J., Munoz, X., Raba, D., Marti, J. and Cufi, X. Yet Another Survey on ImageSegmentation: Region and Boundary Information Integration University of Girona.Institute of Informatics and Applications. Campus de Montilivi s/n. 17071. Girona,Spain

Fu, K. and Mui, J. A survey on image segmentation. Pattern Recognition, 1981.Haralick, R. and Shapiro, L. Image segmentation techniques. Computer Vision,Graphics

and Image Processing, 1985.Huang, Q. and Dom, B. Quantitative methods of evaluating image segmentation Proc. of

the Int. Conf. on Image Processing (ICIP95), 3, 5356, 1995Levine, M. and Nazif, A. Dynamic measurement of computer generated image segmenta-

tions IEEE Trans. on Pattern Analysis and Machine Intelligence, 7, 155164, 1985Martin, D., Fowlkes, C., Tal, D. and Malik,J. A database of human segmented natural im-

ages and its application to evaluating segmentation algorithms and measuring ecologicalstatistics, Proc. Int. Conf. Comp. Vis., vol. 2, pp. 416-425, 2001.

Martin, D., Fowlkes C. and Malik, J. Learning to detect natural image boundaries usinglocal brightness, color and texture cues IEEE Trans. on Pattern Analysis and MachineIntelligence, 26(5), 530549, 2004

Odet, C., Belaroussi, B. and Benoit-Cattin, H. Scalable discrepancy measures for segmen-tation evaluation Proc. of the Int. Conf. on Image Processing (ICIP02), 1, 785788,2002

Shi, J. and Malik, J. Normalized Cuts and Image Segmentation, IEEE Transactions onpattern analysis and machine intelligence, Vol. 22, No. 8, 2000.

Unnikrishnan, R., Pantofaru, C. and Hebert, M. Toward Objective Evaluation of Im-age Segmentation Algorithms, IEEE Transactions on pattern analysis and machineinteligence, Vol. 29, No. 6, 2007.

Unnikrishnan, R., Pantofaru, C. and Hebert, M. A Measure for Objective Evaluation ofImage Segmentation Algorithms

Yang, C., Duraiswami, R. and DeMenthon, D. Mean-Shift Analysis using Quasi-NewtonMethods

Zhang, Y. and Wardi, Y. A recursive segmentation and classification scheme for improvingsegmentation accuracy and detection rate in realtime machine vision applications Proc.of the Int. Conf. on Digital Signal Processing (DSP02), vol. 2, July, 2002