Abstract
Color textures have a unique inter-relationship among its color planes since they contribute information about the same recurring pattern. The average information or entropy is thus presumed to be redundant across the color planes. This is the basis of our paper, which focuses on dimensionality reduction of color texture features by averaging the entropies across multidimensional color planes, while at the same time maintaining the high accuracy of color texture recognition. The mean operation was used in summarizing the original eleven-dimensional difference theoretic texture features for texture classification in Susan and Hanmandlu (IET Image Process 7(8):725–732, 2013). In this work, instead of the mean, we measure the entropy of the features across multidimensional color planes. The non-extensive entropy with the Gaussian information gain is used as the entropy measure for our experiments since it is non-linear and a good indicator of regular patterns in textures. Comparisons with the state-of-the-art prove the efficiency of our approach both in terms of accuracy and the reduced feature dimension.
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Susan, S., Hanmandlu, M. Color texture recognition by color information fusion using the non-extensive entropy. Multidim Syst Sign Process 29, 1269–1284 (2018). https://doi.org/10.1007/s11045-017-0502-z
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DOI: https://doi.org/10.1007/s11045-017-0502-z