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An efficient and effective texture classification approach using a new notion in wavelet theory

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2 Author(s)
Jian-Feng Liu ; Dept. of Comput. Sci., Hong Kong Univ., Hong Kong ; John Chung-Mong Lee

This paper presents a novel multiresolution approach to the classification of textures using wavelets. The approach uses an overcomplete wavelet decomposition, called wavelet-frames, which yields the descriptions of both translation invariance and stability. In order to adapt it to the quasi-periodic properly of textures, we first detect the channels containing dominant information, and then zoom it into these frequency channels for further decomposition. For classification efficiency, we develop a progressive texture classification algorithm, in which the classification process terminates once a suitably chosen discrimination criterion is met. Experiments show that with a minimum number of wavelet frame decompositions and iterations, our proposed approach achieves a 100% correct classification rate on all the texture types tested. It outperforms many of the existing approaches in terms of classification excellence and computational efficiency, and hence appears attractive for real-time applications involving texture-based video/image classification

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:2 )

Date of Conference:

25-29 Aug 1996