Filtering for texture classification: A comparative study
Filtering for texture classification: A comparative study
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DOI：
10.1109/34.761261
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发表时间：
1999-04-01
影响因子：
23.6
通讯作者：
Husoy, JH
中科院分区：
文献类型：
--
作者：
Randen, T;Husoy, JH
在本文中，我们回顾了纹理特征提取的大多数主要过滤方法并进行了比较研究。包括的滤波方法包括定律掩模、环形/楔形滤波器、二进 Gabor 滤波器组、小波变换、小波包和小波帧、正交镜像滤波器、离散余弦变换、特征滤波器、优化的 Gabor 滤波器、线性预测器和优化的有限脉冲响应滤波器。这些特征被计算为滤波器响应的局部能量。突出了过滤的效果，使大多数方法的局部能量函数和分类算法保持相同。作为参考，给出了与两种经典非过滤方法（cc 出现（统计）和自回归（基于模型）特征）的比较。我们根据大量实验对经过测试的方法进行了排名。
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.