Abstract
This paper proposes a multiscale texture classifer which uses features extracted from both magnitude and phase responses of subbands at different resolutions of the dual-tree complex wavelet transform decomposition of a texture image. The mean and entropy in the transform domain are used to form a feature vector. The superior performance and robustness of the proposed classifer is shown for classifying and retrieving texture images from image databases.
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Celik, T., Tjahjadi, T. (2011). Texture Classification and Retrieval Based on Complex Wavelet Subbands. In: Gelenbe, E., Lent, R., Sakellari, G., Sacan, A., Toroslu, H., Yazici, A. (eds) Computer and Information Sciences. Lecture Notes in Electrical Engineering, vol 62. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9794-1_50
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DOI: https://doi.org/10.1007/978-90-481-9794-1_50
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