Efficient image region and shape detection by perceptual contour grouping

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1 Efficient image region and shape detection by perceptual contour grouping Huiqiong Chen Faculty of Computer Science Dalhousie University, Halifax, Canada Qigang Gao Faculty of Computer Science Dalhousie University, Dalhousie University Abstract - Image region detection aims to extract meaningful regions from image. This task may be achieved equivalently by finding the interior or boundaries of regions. The advantage of the second strategy is that once a closure is detected not only its shape information is available, but also the interior property can be estimated with a minimum effort. In this paper, we present a novel method that detects region though region contour grouping based on Generic Edge Token (GET). GETs are a set of perceptually distinguishable edge segment types including linear and non-linear features. In our method, an image is first transformed into GET space on the fly represented by a GET graph. A GET graph presents perceptual organization of GET associations. Two types of perceptual closures, basic contour closure and object contour closure, based upon which all meaningful regions are conducted, are defined and then detected. The detection is achieved by tracking approximate adjacent edges along the GET graph to group the contour closures. Because of the descriptive nature of GET representation, the perceptual structure of detected region shape can be estimated easily based its contour GET types. By using our method, all and only perceptual closures can be extracted quickly. The proposed method is useful for image analysis applications especially real time systems like robot navigation and other vision based automation tasks. Experiments are provided to demonstrate the concept and potential of the method. Index Terms region detection, perceptual closure, GET data I. Introduction An image region is defined as a set of connected pixels with homogenous properties like color or texture, etc, which has obvious contrast to its surroundings. Although region detection plays a significant role in many image analysis applications, it is a challenging task to efficiently obtain meaningful and accurate regions from images, particularly for real-time applications. Region detection methods can be mainly classified into region-based and boundary-based methods [1]. Region-based methods are based on merging similar pixels together to form coherent regions. Region membership decision, under/over segmentation and inaccurate boundaries are their common problems. In [2], detection groups regions for over segmented image by properties of continuity, region ration, etc. To reduce over-segmentation in watershed method, Gaussian convolution with different deviations is used in [3]. Edge information can be used here for refinement, such as seed placement selection, homogeneity criterion control, accuracy improvement [4]. Region-based methods usually treat region as a set of pixels therefore it is difficult to get accurate region shape or object structure from image through these methods. Boundary-based methods employ edge information. Proper edge pixels are grouped into region boundaries. The advantage of these methods is that once a closure forms both region boundaries and its interior properties can be get easily. However, when edge data is provided on pixel level, the method effect will be largely affected by the existence of edge gaps and boundary pixel recognition strategy. Perceptual origination can be used in detection to find perceptual regions/objects [5] [6] but and it always suffers from intensive computation and detecting arbitrary shape regions. In this paper we present a novel method that can detect meaningful regions with arbitrary shape efficiently and speedily. By using innovative perceptual edge features called Generic Edge Token (GET), meaningful regions can be detected promptly through perceptual region contour detection. Perceptual region contour closures are defined first to represent meaningful regions and then be detected through closure detection in GET space. Because of the simplicity of GET representation, and inherent structure information carried by each feature, the perceptual region shape can be obtained easily from region as well as region boundary and interior attributes. Therefore, our method is suitable for image analysis applications especially for real-time systems. This paper is organized as follows: Section II introduces Generic Edge Tokens. Section III presents perceptual region hierarchy and perceptual region representation. Contour closure detection method is provided in section IV. Experiments are given in section V. Section VI provides conclusions and future work. II. Generic Edge Tokens Generic Edge Tokens (GETs) are perceptually significant image primitives which represent classes of qualitatively equivalent structure elements. A complete set of GETs includes both Generic Segments (GS) and curve partition points (CPP), Each GS is perceptually distinguishable edge segment with linear or nonlinear features while each CPP is junction of GSs. Gao and Wang proposed a curve partition method in a very generic form which performs partition following the similar objective in human visual perception. The image is scanned horizontally and vertically with an interval to find strong edge pixels as tracking starting pixels then edge pixels on the traces are selected according to the GS definitions. This method is robust with low time expense. Detail explanation of GET detection can be found in [7]. Fig. 1 (a) shows curve partition examples.

2 Fig. 1. Curve partition examples. A GS is a segment in image with monotonic increasing or decreasing. GSs can be perceptually classified into eight categories as Fig. 1 (b) shows. Each type of GSs should satisfy properties described by the monotonicities of tangent function y= f(x) and its inverse function x = ϕ(y), as Table 1 illustrates. The monotonicity is checked along each GS. A CPP is a perceptually significant point where adjacent GSs meet and curve turning takes place [8]. No intersection among GSs exists besides CPPs. CPP can group GSs into perceptual structures. There are four basic categories of CPPs according to the conjunction types as Fig. 1 (c) shows. The intrinsic perceptual features of GETs will facilitate further detection of perceptual closures formed by GETs and ease the extraction of perceptual shape from regions described by GET closures. III. Perceptual region hierarchy and representation According to MPEG-7 standard, region or object shape can be described by Contour Shape descriptors based on its contour [9]. Therefore contour closure, formed by region boundaries, is desirable representation for meaningful regions. It possesses both low-level features and perceptual concepts: closure interior pixels contain low level attributes; closure boundary contains structure/shape information at higher level. A. Perceptual region concept hierarchy From perceptual view, meaningful regions arise from objects, and then can be represented by object outline and object basic inner parts (called basic regions). An object is constituted by one or multiple basic regions. As Fig. 2 shows, object A is built by 3 basic regions, whose contours can be represented by basic contour closures e 1 e 2 e 3 e 4, e 2 e 5 e 6 e 7, e 3 e 7 e 8 e 9 respectively, all of which are formed by GETs. Other closures are constituted by basic contour closures, among which e 1 e 5 e 6 e 8 e 9 e 4 represents the object outline. Therefore all closures in image formed by GETs can be classified into three types according to region contour properties: basic contour closure which represents the contour of basic region; object contour closure which represents the contour of object outline or separated object component outline; other composite closure. All meaningful regions in an object can be represented by first two types of GET closures while GET closures can be described by GET organizations. As Fig. 2 illustrates, object B has a separate hole inside, which can be regarded as a separate object component constituted by one TABLE 1. Definitions of GS types; (M+ and M- represent monotonic increasing and decreasing properties while c mean a constant) GS type Properties LS1 LS2 LS3 LS4 CS1 CS2 CS3 CS4 f(x) c n/a M+ M- M+ M- M- M+ ϕ(y) n/a c M+ M+ M+ M- M- M- f (x) 0 c c M- M- M+ M+ ϕ (y) 0 c c M+ M+ M- M- single basic region. The remaining part of B other than the hole is another basic region, whose contour can be described by the basic contour closure e 1 e 2 e 3 e 4 along with the inside object contour closure e 5 e 6 e 7 e 8, which represents outline of the object component hole. From analysis above we can conclude that, basic contour closures and object contour closures contain most perceptual information of meaningful regions while other composite closures do not have much meaning. Definition 1: Meaningful regions in image can be represented by two types of perceptual contour closures: basic contour closures and object contour closures. A basic contour closure is a basic GET closure which can not be split into other closures; an object contour closure is a GET closure representing the outline of an object or object individual component. (Assume no occlusion occurs). This definition has several advantages. First, it leads to a hierarchical descriptor for image content: Image content can be represented by objects, while an object can be described by the hierarchy in Fig. 2. Second, both types of perceptual closures are supported in MPEG-7 frame by contour shape descriptors [10]. Last, it makes sure that useful perceptual closures will be picked up in detection and meaningless composite closures will not be detected anymore. A region/closure concept hierarchy is constructed in Fig. 2. In this hierarchy, GETs can be organized perceptually into contour closures, which can build meaningful regions in objects. To find perceptual contour closures from all GET data, we use a GET graph to code perceptual organization of GET associations in image. Fig. 2. Perceptual region/closure concept hierarchy

3 B. Perceptual organization representation by GET graph A graph named GET graph can be derived from preextracted GET data to code the perceptual structure of image. An image is transformed into GET space represented by a special graph named GET graph. Therefore, perceptual contour closures, i.e., basic contour closures and object contour closures, can be detected by perceptual cycle search in GET graph when exploring the correspondence between the GET closures and graph cycles [11]. For the purpose of real time processing, GET graph is reduced and search starting edges are selected before search to avoid duplicate and reduce computation burden. Starting from selected edges, novel perceptual contour closure detection method is applied to get all perceptual closures from GET graph then results are classified into perceptual types. Taking advantage of the perceptual GET class data, the image can be transformed into GET space represented by a GET graph. With all GETs extracted from image, GET graph can be constructed by converting GSs and CPPs to graph edges and vertices respectively. Each edge in GET graph presents a perceptually distinguishable edge segment in image while the organization of GET graph represents the perceptual structures in image. If no occlusion occures, each connected component in GET graph corresponds to an object/individual object component in image constituted by basic inner parts, which can be represented by basic contour closures. Therefore basic contour closures in image one-to-one correspond to basic cycles in GET graph while object contour closures oneto-one correspond to outlines of graph connected components. For convenience sometimes we do not distinguish between the terms in the context of the paper. GET graph has some unique characters: (1) Besides representing graph structure, the edges and vertices in GET graph also represent real segments and their junction points in image. (2) According to GET properties, no intersection among edges should exist besides edge endpoints. GET graph can be constructed as follows: let G= (V, E) be an undirected graph, where V is the set of vertices and E is the set of edges in G. Initially V= and E=. For each GS i (0 i< m, m is number of GSs in image), insert it into E as edge e i. For each CPP j (0 j < n, where n is number of CPPs in image), insert it to V as vertex v j. In practice, there always exist edge gaps in image as Fig. 3 (a) shows. The gaps may be caused by image noise or broken edges derived from edge detector. To bridge possible gaps, we amend the concept of adjacent for GET graph and regard close edges within a small common region as adjacent: For edges e i and e j, if e i s endpoint v m is close to e j s endpoint v n, normalized distance between v m and v n is defined l( vm) + l( vn) dist (vm, vn ) = d( v, ) 2 m v (1) n [ l( vm) + l( vn )] + δ where l(v m ), l(v n ) are length of longest edges among all edges connecting to v m, v n respectively; δ is mean of length of all edges E, d(v m, v n ) is the distance between v m and v n. If dist(v m, v n ) threshold Τ, the gap of e i and e j should be bridged. A virtual vertex m is used as combination of real vertices v m, v n. m is virtual intersection of e i and e j obtained by extending endpoints v m and v n from e i and e j respectively. Therefore, m is a substitute for v m, v n as endpoint of e i and e j. Definition 2: Graph vertex v j (no matter v j is a real vertex or virtual vertex) is incident with edge e i iff: v j is e i s endpoint. Edges e i, e j are adjacent iff e i and e j : (a) share a real vertex as common endpoint; or (b) share a virtual vertex as common endpoint within threshold T. we denote this as e i ~ e j. The degree of vertex v j, denoted as deg(v j ), is the number of edges incident with v j within T. Fig. 3 (b) illustrates GET graph construction. If dist (v 0, v 1 ) T, the edge gap between e 1 and e 10 /e 12 is so small that it can be bridged by virtual vertex m 1. e 1, e 10, e 12 are adjacent by m 1, deg(m 1 )= 3; otherwise, this gap can not be ignored. e 1 is not adjacent to e 10 /e 12. deg(v 0 )=2 and deg(v 1 )= 1. Similarly, gap between e 4 and e 5 can be bridged by m 2 if dist(v 5, v 6 ) T. GET space is reduced by graph reduction, which remove noise edges not belonging to graph cycle. It decreases future search burden and false closure. An edge e i with endpoints v m and v n is a noise edge iff: (a) deg (v m ) = 1; or (b) besides e i, v m is incident with only noise edges; or (c) deg (v n ) =1; or (d) besides e i, v n is incident with only noise edges. Fig. 4(b) shows a GET graph and (c) shows graph reduction. IV. Perceptual contour closure detection Perceptual closure detection can be carried out using GETs pre-extracted from image, as Fig. 5 shows. After graph reduction, every edge in GET graph must be element of some closures. A perceptual closure in image can be detected by finding corresponding cycle in GET graph. Fig. 4 (a) Original image; (b) GET graph; (c) GET graph reduction Closure Definition Image GET Detection GET graph Construction & Reduction Closure Detection Perceptual GET closures (regions) (a) GETs in image with edge gaps; (b) GET graph with virtual vertices Fig. 3. Construct graph in GET space using GET data. Search starting edge selection Closure detection by cycle search Fig. 5. Perceptual contour closure detection architecture Result closure classification

4 Staring from one endpoint of an edge then tracking along adjacent edges, a graph cycle can be formed if a path back to starting point is found in graph. All closures may be detected from GET graph by this method when starting from closure edges. However, this method has two problems. First, proper starting edges should be selected instead of starting search from every graph edge. This can reduce computation cost and duplicate, avoid time expense increasing greatly with graph edge number. Second, search strategy is needed in tracking to guarantee the results be perceptual closures. Spanning trees are employed for the first problems. For the second problem, a new perceptual search strategy is proposed. A. Starting edge selection Spanning tree is a connected sub-graph with tree structure including all vertices of a graph connected component. Lemma 1: all perceptual closures can be obtained by tracking in graph starting from edges not belonging to spanning trees. That is, edges not belonging to spanning trees should be selected as starting edges. Proof of Lemma 1: Let G i =(V i, E i ) be the ith connected component of GET graph, (V i, E i ) be set of vertex and edges for the spanning tree of G i, N i = E i - E i. We need to prove (a) e N i, e must be element of a basic contour closure; (b) each basic contour closure in G i must have at least one edge e such that e N i ; (c) the object contour closure corresponding to component outline must have at least one edge e so that N i. (a) As spanning tree, V i =V i ; (V i, E i ) contains no cycle. e N i with endpoints v x v y, there have v x V i, v y V i and one single path p between (v x, v y ) in the tree. If e is added to the tree, e combines with path p forming a cycle. This cycle either is a basic cycle or is built by basic cycles. e belongs to one of basic cycles, i.e., e belongs to a basic contour closure. (b) Since spanning tree contains no cycle, any basic cycle in (V i, E i ) must have at least one edge e excluded from (V i, E i ) to avoid forming this cycle, i.e., e N i. (c) The proof of (c) is similar with (b). From (a)-(c), we can conclude that Lemma 1 is true. B. Perceptual Cycle search algorithm In connected component i, basic cycle is cycle without sub-cycle inside and component outline is a cycle without any cycle outside. Starting from e i N i, which must belong to some basic cycle of G i, basic contour closure can be formed by basic cycle search in graph. The search tracks along adjacent edges by always selecting the innermost edge in given direction (anticlockwise/clockwise) from all candidates in each step of path selection. If e i is on component outline, object contour closure can also be extracted by selecting outmost edge in given direction (which can be considered as selecting innermost edge in the other direction) in each step. A new closure detection method is proposed to find all perceptual closures by perceptual cycle search algorithm. This algorithm is based on the following hypothesis which will be proven by lemma 2: e i N i with endpoints v x and v y, starting from v x of e i, a perceptual closure (object contour closure or basic contour closure) can be found by cycle search in graph, which always selects innermost edge in some direction from all adjacent edges as next edge in each step of selection. Since innermost edge is direction sensitive, the cycle search must keep the same direction in each step. Fig. 6 (a) illustrates cycle search. If a clockwise search starts from v 7 of e 7, e 7 is current edge while v 7 is current vertex. e 5, e 6 ~ e 7 by current vertex v 7. As the clockwise innermost edge for e 7, e 5 is selected to be next cycle edge. In next step, e 5 is current edge while e 6 s another endpoint m 2, is the current vertex. e 4 ~ e 5 by m 2 so e 4 is selected. Search stops when new selected e 10 meets e 7 at v 9 and basic contour closure e 7 e 5 e 4 e 3 e 2 e 1 e 10 forms. Another basic contour closure e 7 e 6 e 8 e 9 can also be formed by anticlockwise search starting from v 7 of e 7. Object contour closure can be extracted as well. Starting from v 10 of object contour edge e 11, object contour closure e 11 e 8 e 6 e 5 e 4 e 3 e 2 e 1 e 12, which is constituted by clockwise outermost edges, can be got by anticlockwise cycle search. Algorithm 1: let e i with endpoints v x, v y be starting edge, the cycle search algorithm in direction d can be presented as: Step 1: initially, current edge ce:=e i, current vertex cv:=v x. Let C be set of closure edges, C={e i }. Step 2: if ce= e i and cv=v y, search stops and perceptual closure forms; otherwise perform step 3~4; Step 3: for each edge e j adjacent to ce by cv, calculate the included angle from cv to e j in direction d. Select the adjacent edge with minimal included angle as new selected edge ne, C=C {ne}; Step 4: update cv and ce; cv:= ne s another endpoint which is not incident with ce; ce:=ne; go to step 2. Lemma 2: the closure extracted using perceptual cycle search algorithm must be a perceptual closure. Proof of Lemma 2: let C be the cycle search result in direction d starting from e i. Here we prove C must represent either a basic contour closure or an object contour closure. (1) If e i is not on component outline, C can not be an object contour closure. There must exist more than one basic cycles sharing e i ; assume that C is not a basic cycle, there must exist basic cycles C inside C and C outside of C sharing e i with C. If C C and C have identity edges e i ~ e j at first several steps of search, let e m, e n and e l be next edges selected in C, C and C respectively. (a) If e m e n and e l are different edges, e m should be located inside the area between e j and e m. Thus e m can not be the innermost edge against e j in whatever search direction, which contradicts the fact that edges in C should be the innermost edge in direction d. (b) If (a) Detect perceptual closures (b) Simulate closure by polygon Fig. 6. (a) shows the process of perceptual closures detection. Dashes in (a) are starting edges and arrows indicate search direction. Polygon in (b) simulates the closure formed by clockwise search starting from v 7 of e 7. Dashes in (b) indicate polygon edges different from graph edges.

5 e m is same to e n but different with e l ; let e k be the last identical edge for C and C, e p and e q be next edges for C and C. e q is inside C and e l is outside C. No matter d is clockwise or anticlockwise, either e p is not the innermost edge against e k (e q is the innermost in stead) or e m is not the innermost edge against e j (e n is the innermost). This contradicts to previous fact. (c) If e m = e l but different with e n ; proof is similar to (b). Summarizing (a)~(c), if e i is on component outline, the prior assumption can not be true. C must be a basic contour closure. (2) If e i is on component outline; proof is similar to (1). From (1)-(2), we can conclude that Lemma 2 is true. C. Perceptual closure detection via cycle search Perceptual closure detection can be described as follows: Step 1: Construct GET graph and remove noise graph edges. Step 2: Find all connected components in the GET graph. For each component, perform step 3. Step 3: Extract a spanning tree. Starting form each edge e i not in the tree, perform step 4. Step 4: perform both clockwise and anticlockwise closure searches by applying perceptual cycle search algorithm. It can be easily concluded from lemma 2 that using this detection method, two closures can be extracted by clockwise and anticlockwise searches starting from a given edge e, and there are two possibilities for extracted closures: (1) If e is not on component outline, both of the detected closures are basic contour closures; (2) If e is on component outline, the two closures have one basic contour closure and one object contour closure. Lemma 3: the perceptual closure detection method is valid because by using this method, (1) all extracted closures are perceptual closures; (2) all perceptual closures are extracted. Proof of lemma 3: (1) has been represented in lemma 2. (2) First we prove that object contour closure must be detected. Based on lemma 1, there must have an object contour closure edge e j N i as starting edge. Starting from this edge, the two results must have one object contour closure. Second we prove that all basic contour closures must also be extracted. Assume there is a basic contour closure C that cannot be detected by our method, C must have an edge e j N i. If e j is not on the component outline, C must share e j with two other basic contour closures C and C, both of which are detected by cycle search starting from e j. No intersection exist among the three closures, so C must be outside the area of (C C ) while e j is an edge inside the area of (C C ). This contradicts with the fact that e j is an edge of C. If e j is on component outline; C shares e j with basic contour closure C and object contour closure C. The areas of C and C do no intersect while both of them are inside C. Therefore C should be inside the area of (C -C ), in which e j can not be included. This also conflicts with previous fact. Thus this kind of C can not exist no matter e j s type. D. Closure classification Perceptual closure detection method above finds all perceptual closures without concerning closure types. The result closures should be classified into two types: basic contour closure and object contour closure. The classification can be achieved by summing up all included angles of a polygon which simulates the extracted closure. The polygon can be formed during cycle search by simulating the selected edge in each step as a straight line between its two endpoints. Included angles between simulation lines with search direction are recorded. Fig. 6 (b) shows an example: in clockwise search starting from v 7 of e 7, the first selected edge is e 5 so the clockwise included angle from e 7 to e 5 is recorded as θ. The next v7v9 v7m2 selected edge e 4 is simulated by line from m 2 to v 4 so θ is recorded. The polygon forms when θ v9m1 v9v7 is recorded. m2v7 m2v4 For an n-edge closure, let θ Total be sum of all n included angles recorded during simulation. If the closure is basic contour closure, recorded included angles should be interior angles of polygon since the simulation passes inside closure in each step, as arrows in Fig. 6(b) shows. θ Total = (all interior angles of polygon) = 180*(n-2). If the closure is object contour closure, the included angles should be exterior angles of polygon thus θ Total = 180*(n+2). Therefore we conclude: If θ Total = 180*(n-2), the closure is basic contour closure; and If θ Total = 180*(n+2), the closure is an object contour closure. where n is the number of edges in the closure. Fig. 7 illustrates the process of closures detection and classification. V. Experiment Results To evaluate validity of our method, algorithms are implemented using C++. Sun Fire 4800, a multi-user UNIX server running Solaris 8 with 16 GB RAM, 4 UltraSPARC-III processors at 900 MHz, is used in test. Server load average = 2.64 during test. The test images, whose sizes range from 256*256 to 512*512 pixels, include both synthetic image and real images. The experiment first extract GETs from image; then detect regions based on GET data. In our test, average times of GET extraction and region detection are 418 and 447 millisecond. The number of perceptual closures in image and correct/error/ missing in the results are determined manually with human perception. Tiny regions are omitted as noises. Experiment results are listed in Table 2. Average detection correctness = 91.13%. Experiments show that our method can get arbitrary region shapes with high correctness and short process time, which exhibits its potential in image analysis applications. Fig. 8 gives detection examples. The error/missing may be caused by false edge selection in search: (1) although normalized threshold is used to bridge gaps between closure edges, it can not distinguish edge gaps between relevant edges with cracks in irrelevant edges by perceptual view. Larger threshold, although bridges larger gaps, raises the risk of taking unrelated edges as adjacent, thus increases false detection. (2) Detection depends heavily on GET data got from GET tracker. It can not find a region contour if one contour edge is missing detected in GET tracking, i.e., it can not recovery lost data by knowledge in closure detection. (3) The GETs derived from image noises may also mix up with valid GETs thus mislead search.

6 Fig. 7. Closure extraction and classification for Fig. 4(a); left-most figure shows all contours; other small figures show all basic contour closures. Image No Fig. 8. Detection result examples. Each row from left to right: original image; extracted GETs; region contour formed by GETs; filled regions. TABLE 2. Experiment results. Average correctness= 91.13%. correct Error missing closures in image Percentage of correct % % % % % % % VI. Conclusions and Future Work In this paper, we propose a GET based method for extracting meaningful regions from image through contour closure detection. Perceptual closures are defined to represent meaningful regions and then be extracted from GET data. The proposed method has following advantages. (1) It provides real-time region segmentation in which region shape structure can be extracted as well. (2) It achieves high accuracy of detected regions with low computation cost. (3) The method is suitable for segmenting objects with arbitrary shapes. (4) It can detect both object contours and their components, based upon which all meaningful regions and objects can be conducted hierarchically as Fig. 2 shows. The possible extension of this method may include the follows. Other features, such as color and texture, can be added in the process to increase the robustness of detection in order to cover a wider range of applications. Meanwhile the added features would provide with a complete set of region descriptions. A region may be encoded with all attributes, i.e. shape, color, and texture. The segmentation technique may support various application domains include robot navigation, surveillance motion analysis, image retrieval, etc. References [1] A. Narendra, On Detection and Structure Representation of Multiscale Low-Level Image ACM Computmg Surveys, vol. 27, no. 3, pp , 1995 [2] T. Tamaki, T. Yamamura, and N. Ohniski, Image Segmentation and Object Extraction based on Geometric features of Regions, SPIE Conf. on Visual communication & image processing, vol. 3653, pp , 1999 [3] J. M. Gauch, Image Segmentation and Analysis via Multi-scale gradient watershed hierarchies, IEEE Trans. on Image Processing, vol. 8, No. 1, pp , 1999 [4] J. Freixenet, X. Munoz, D. Raba, J. Mart, and X. Cuf, Yet Another Survey on Image Segmentation: Region and Boundary Information Integration, Proc. of the 7th European Conf. on Computer Vision, pp , 2002 [5] A. L. Ralesc., and J. G. Shananhan, Pereptual Organisation for inferring Object Boundaries in an Image, Pattern Recognition, Vol. 32, pp. 1923~1933, 1999 [6] S.Sarkar and K. Boyer, Perceptual Organization in Computer Vision: A review and a proposal for classificatory structure, IEEE Trans. on Systems, Man & Cybernetics, Vol.23, No. 2, pp. 382~398, 1993 [7] Q. Gao, and A. Wong, Curve Detection based on Perceptual Organization Pattern Recognition, Vol. 26, No. 1, pp. 1039~ 1046, 1993 [8] X. Zheng, and Q. Gao, Generic Edge Tokens, Representation, Segmentation and Grouping, Proc. of the 16th International Conf. on Vision Interface, pp , 2003 [9] MPEG-7 Overview, appears in /standards/mpeg-7/mpeg-7.htm, Mar, 2003, access at Oct, [10] T. Sikora, the MPEG-7 Visual Standard for Content Description - An Overview, IEEE TRANS. on Circuits and Systems for Video Technology, Vol. 11, No. 6, pp. 716~719, 2001 [11] Q. Iqbal and J. K., Retrieval by Classification of Images containing large Manmade Objects using Perceptual Grouping, Pattern Recognition, Vol. 35, No. 7, pp , 2002