High-Order Circular Derivative Pattern for Image Representation and Recognition

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Zhao, S
Gao, Y
Caelli, T
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IAPR

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2010
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617121 bytes

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Istanbul, Turkey

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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.

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Proceedings - International Conference on Pattern Recognition

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© 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.

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Computer vision

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