Multi-Object Shape Retrieval Using Curvature Trees

Abstract

With the increasing number of images generated every day, textual annotation of images becomes impractical and inefficient. Thus, content-based image retrieval (CBIR) has received considerable interest in recent years. For comparing images, CBIR uses generic image features which are traditionally either intensity-based (color and texture) or geometrybased (shape and topology); the latter is generally less developed than the former. A common limitation of the existing geometry-based retrieval systems is not considering simultaneously both shape and topology of image objects (or components) which may reveal important properties of the scene being analyzed. This work presents a geometry-based image retrieval approach for multi-object images. We commence with developing an effective shape matching method for closed boundaries. Then, a structured representation, called curvature tree (CT), is introduced to extend the shape matching approach to handle images containing multiple objects with possible holes. We also propose an algorithm, based on Gestalt principles, to detect and extract high-level boundaries (or envelopes), which may evolve as a result of the spatial arrangement of a group of image objects. At first, a shape retrieval method using triangle-area representation (TAR) is presented for non-rigid shapes with closed boundaries. The TAR is a 2D matrix that utilizes the areas of the triangles formed by the boundary points to measure the convexity/concavity of each point at different scales (or triangle side lengths). This representation is effective in capturing both local and global characteristics of a shape, invariant to translation, rotation, scaling and shear, and robust against noise and moderate amounts of occlusion. For matching, two algorithms are introduced. The first algorithm matches concavity maxima points extracted from TAR image obtained by thresholding the TAR. In the second matching algorithm, dynamic space warping (DSW) is employed to search efficiently for the optimal (least cost) correspondence between the points of two shapes. Then, a dissimilarity measure is derived based on the optimal correspondence. Experimental results using the MPEG-7 CE-1 database of 1400 shapes show the superiority of our method over

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Cite this paper

@inproceedings{Alajlan2006MultiObjectSR, title={Multi-Object Shape Retrieval Using Curvature Trees}, author={Naif Alajlan}, year={2006} }