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Wavelet-Based Image Texture Classification Using Local Energy Histograms

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2 Author(s)
Yongsheng Dong ; Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, China ; Jinwen Ma

In this letter, we propose an efficient one-nearest-neighbor classifier of texture via the contrast of local energy histograms of all the wavelet subbands between an input texture patch and each sample texture patch in a given training set. In particular, the contrast is realized with a discrepancy measure which is just a sum of symmetrized Kullback-Leibler divergences between the input and sample local energy histograms on all the wavelet subbands. It is demonstrated by various experiments that our proposed method obtains a satisfactory texture classification accuracy in comparison with several current state-of-the-art texture classification approaches.

Published in:

IEEE Signal Processing Letters  (Volume:18 ,  Issue: 4 )