International Journal of Computer Science and Applications
c 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, Romania
bogdan.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, Romania
ganea 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 experimental results from the error measurement point of view.
We introduce a new method of salient object recognition that takes into consideration
color 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 to
analyze the consistency of different segmentations provided by several well known algorithms. 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 evaluation
methods.
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
36
2
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 challenging 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 segmentation 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 segmentation 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 Segmentation (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 segmentations in the Berkeley segmentation data set [Berkeley (2003)]. The dataset has
3
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 consistency 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 segmentation as the reference. In these methods, the segmentation performance is
usually measured by some contextual and perceptual properties, such as the homogeneity 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 modelbased [Carson (2002)] or visual feature-based [Fauqueur (2004)] approaches. A distinct category of region-based segmentation methods that is relevant to our approach 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)].
4
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 homogeneity and inter-object disparity have been proposed in [Levine (1985)]. A combined 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 segmentation. 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 customized 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 measures are designed to be tolerant to refinement. A methodology for evaluating the
performance of boundary detection techniques with a database containing groundtruth data was developed in [Martin (2004)]. It is based in the comparison of the
detected boundaries with respect to human-marked boundaries using the PrecisionRecall 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 segmentation 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 contribution 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 segmentation algorithm [Comaniciu (2002)], Efficient Graph-Based segmentation algorithm [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,
5
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 efficiency, 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
6
adjacent regions is based on the difference between the internal contrast and the external 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 segmentation 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 Borůvka’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 perform 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 segmentation 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 S 0 =({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 Cxt−1
be the connected component containing point xi on iteration t − 1
i
and li = maxmst Cxt−1
be the longest edge in the minimum spanning tree of
i
t−1
Cxi . Likewise for lj .
(c) Merge Cxt−1
and Cxt−1
if:
i
j
{
|et | < min li +
k
Cxt−1
i
, lj +
k
Cxt−1
j
}
(1)
(4) S = S m .
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
7
neighboring pixels. The edge weight w(u, v) describes the affinity between two vertices 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 experiment 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:
N cut(A, B) =
cut(A, B)
cut(A, B)
+
assoc(A, V ) 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 directly 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 methods 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 segmentation.
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
8
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 segmentations. 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 segmentation 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 inconsistent. 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 boundaries.[Unnikrishnan (2000)]
We will evaluate the performance of our algorithm on the Berkeley Segmentation 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,
9
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 segmentations 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
10
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:
{
}
∑
∑
1
GCE(S1 , S2 ) = min
E(S1 , S2 , pi ),
E(S2 , S1 , pi )
n
i
i
LCE(S1 , S2 ) =
1∑
min{E(S1 , S2 , pi ), E(S2 , S1 , pi )}
n i
(4)
(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 evaluate 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 undersegmented 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-
11
tinct points that define the errors for each approach. For N ormalized 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 Ef f icient 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 [Comaniciu (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 provided 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 results 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 segmentation results for the studied algorithms via the errors metrics.
In order to achieve this, we considered the human segmentation as the groundtruth segmentation and compared each algorithm with it, measuring the error metrics 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 algorithm. 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-
12
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 partitioned 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
13
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 evaluation 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
15
Fig. 9. GCE for Mean-Shift
Fig. 10. LCE for Mean-Shift
mechanism, improving the evaluation metrics and enlarging the reference segmentation methods. We will also continue to work on integration of syntactic visual
information into a semantic level of a semantic image processing and indexing system.
Acknowledgment
This work has been supported by CN CSIS−U EF ISCSU , project number P N II−
IDEI code/2008.
16
Fig. 11. GCE overall comparison
Fig. 12. LCE overall comparison
Fig. 13. Comparative segmentation results A : Human Segmentation, Graph-based Salient Object
Detection, 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 Object
Detection, Normalized Cuts, Efficient Graph-Based, Mean-Shift
Fig. 15. Comparative segmentation results C: Human Segmentation, Graph-based Salient Object
Detection, 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 using 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. Intl
Conf. 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, New
York, 2000.
Falah, R., Bolon, P., Cocquerez, J.A region-region and region-edge cooperative approach of
image segmentation. International Conference on Image Processing. Volume 3., Austin,
Texas 470474, 1994.
Fauqueur, J. and Boujemaa, N. Region-based image retrieval: Fast coarse segmentation
and 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 Image
Segmentation: 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, 1995
Levine, M. and Nazif, A. Dynamic measurement of computer generated image segmentations IEEE Trans. on Pattern Analysis and Machine Intelligence, 7, 155164, 1985
Martin, D., Fowlkes, C., Tal, D. and Malik,J. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological
statistics, Proc. Int. Conf. Comp. Vis., vol. 2, pp. 416-425, 2001.
Martin, D., Fowlkes C. and Malik, J. Learning to detect natural image boundaries using
local brightness, color and texture cues IEEE Trans. on Pattern Analysis and Machine
Intelligence, 26(5), 530549, 2004
Odet, C., Belaroussi, B. and Benoit-Cattin, H. Scalable discrepancy measures for segmentation 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 on
pattern analysis and machine intelligence, Vol. 22, No. 8, 2000.
Unnikrishnan, R., Pantofaru, C. and Hebert, M. Toward Objective Evaluation of Image Segmentation Algorithms, IEEE Transactions on pattern analysis and machine
inteligence, Vol. 29, No. 6, 2007.
Unnikrishnan, R., Pantofaru, C. and Hebert, M. A Measure for Objective Evaluation of
Image Segmentation Algorithms
Yang, C., Duraiswami, R. and DeMenthon, D. Mean-Shift Analysis using Quasi-Newton
Methods
Zhang, Y. and Wardi, Y. A recursive segmentation and classification scheme for improving
segmentation accuracy and detection rate in realtime machine vision applications Proc.
of the Int. Conf. on Digital Signal Processing (DSP02), vol. 2, July, 2002