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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/279

Title: Maximal disk based histogram for shape retrieval
Authors: Choi, Wai-Pak
Lam, Kin-man Kenneth
Siu, Wan-chi
Subjects: Shape descriptor
Skeletonization
Maximal disk
Histogram distance
Shape retrieval
Issue Date: 2003
Publisher: IEEE
Citation: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing : proceedings : April 6-10, 2003, Hong Kong Exhibition and Convention Centre, Hong Kong, p. III9-III12
Abstract: In this paper, we propose a robust and efficient representation scheme for shape retrieval, which is based on the normalized maximal disks used to represent the shape of an object. The maximal disks are extracted by means of a fast skeletonization technique with a pruning algorithm. The logarithm of the radii of the normalized maximal disks is used to construct a histogram to represent the shape. The retrieval performance of this maximal disk based histogram approach is compared to other methods, including moment invariants, Zernike moments, and curvature scale-space. Experimental results show that our proposed representation scheme outperforms the other methods under affine transformation and different noise levels.
Rights: © 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Conference Paper
URI: http://hdl.handle.net/10397/279
ISBN: 0780376633
Appears in Collections:EIE Conference Papers & Presentations

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