High-Order Circular Derivative Pattern for Image Representation and Recognition

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Title High-Order Circular Derivative Pattern for Image Representation and Recognition
Author Zhao, Sanqiang; Gao, Yongsheng; Caelli, Terry
Publication Title Proceedings of the 20th International Conference on Pattern Recognition (ICPR 2010)
Editor IAPR
Year Published 2010
Place of publication United States
Publisher IEEE Computer Society
Abstract Micropattern based image representation and recognition, e.g. Local Binary Pattern (LBP), has been proved successful over the past few years due to its advantages of illumination tolerance and computational efficiency. However, LBP only encodes the first-order radial-directional derivatives of spatial images and is inadequate to completely describe the discriminative features for classification. This paper proposes a new Circular Derivative Pattern (CDP) which extracts high-order derivative information of images along circular directions. We argue that the high-order circular derivatives contain more detailed and more discriminative information than the first-order LBP in terms of recognition accuracy. Experimental evaluation through face recognition on the FERET database and insect classification on the NICTA Biosecurity Dataset demonstrated the effectiveness of the proposed method.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1109/ICPR.2010.550
Copyright Statement Copyright 2010 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.
ISBN 1051-4651
Conference name The 20th International Conference on Pattern Recognition (ICPR 2010)
Location Istanbul, Turkey
Date From 2010-08-23
Date To 2010-08-26
URI http://hdl.handle.net/10072/36156
Date Accessioned 2010-10-11
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Computer Vision; Pattern Recognition and Data Mining
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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