Filtering for texture classification: A comparative study

被引:999
作者
Randen, T
Husoy, JH
机构
[1] Schlumberger Geco Prakla, N-4001 Stavanger, Norway
[2] Stavanger Coll, Stavanger, Norway
关键词
texture classification; image processing; filtering; survey;
D O I
10.1109/34.761261
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we review most major filtering approaches to texture feature extraction and perform a comparative study. Filtering approaches included are Laws masks, ring/wedge filters, dyadic Gabor filter banks, wavelet transforms, wavelet packets and wavelet frames, quadrature mirror filters, discrete cosine transform, eigenfilters, optimized Gabor filters, linear predictors, and optimized finite impulse response filters. The features are computed as the local energy of the filter responses. The effect of the filtering is highlighted, keeping the local energy function and the classification algorithm identical for most approaches. For reference, comparisons with two classical nonfiltering approaches, cc-occurrence (statistical) and autoregressive (model based) features, are given. We present a ranking of the tested approaches based on extensive experiments.
引用
收藏
页码:291 / 310
页数:20
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