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Wavelet-Based Image Texture Classification Using Local Energy Histograms
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DOI：10.1109/LSP.2011.2111369.png)
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摘要
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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.
Keyword:
Energy histogram
one-nearest-neighbor classifier
symmetrized Kullback-Leibler divergence (SKLD)
texture classification
wavelet transform
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期刊
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9.6
论文数:
1.1W
被引数:
1.7W
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