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Gabor 滤波器和ICA支持的无监督纹理分割

Unsupervised Texture Segmentation Using Gabor Filters and ICA

  • 摘要: 纹理分割已经取得了很大的进展,但仍然缺乏一个轻便的解决方案.建立了一个无监督纹理分割框架,其核心是将Gabor滤波器所提取的特征视为统计量,用独立分量分析(ICA)整合特征,并用独立分量作为新的纹理特征,避开了Gabor滤波器参数选择的难题.实验结果表明,ICA比主分量分析更利于纹理特征重整.采用该方法对大多数自然纹理能够得到满意的分割结果.

     

    Abstract: Despite the great progress of unsupervised texture segmentation, it still lacks a portable and unified solution to address the diversity of textures. A new framework to gear to the need is presented to take texture features extracted directly by Gabor filters as statistic values. The distinct points are:(1) the framework avoids the difficult problem of selecting parameters of Gabor filters;(2) it integrates texture features using independent component analysis(ICA);and (3)it treats the independent components as new texture features. Experiments on mosaic natural texture images show the proposed framework gives satisfactory results compared to those based on principal component analysis.

     

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