Texture Classification Using Discriminant Wavelet Packet Subbands
نویسنده
چکیده
This paper addresses the issue of selecting features from a given wavelet packet subband decomposition that are most useful for texture classification in an image. A functional measure based on Kullback-Leibler distance is proposed as a way to select most discriminant subbands. Experimental results show a superior performance in terms of classification error rates.
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