Texture CLassification Using Compressed Sensing

Li Liu, P. Fieguth
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引用次数: 10

Abstract

This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing and bag of words model, suitable for large texture database applications with images obtained under unknown viewpoint and illumination. At the feature extraction stage, a small set of random features are extracted from local image patches. The random features are embedded into the bag of words model to perform texture classification. Random feature extraction surpasses many conventional feature extraction methods, despite their careful design and complexity. We conduct extensive experiments on the CUReT database to evaluate the performance of the proposed approach. It is demonstrated that excellent performance can be achieved by the proposed approach using a small number of random features, as long as the dimension of the feature space is above certain threshold. Our approach is compared with recent state-of-the-art methods: the Patch method (Varma and Zisserman, TPAMI 09), the MR8 filter bank method (Varma and Zisserman, IJCV 05) and the LBP method (Ojala et al., TPAMI 02). It is shown that the proposed method significantly outperforms MR8 and LBP and is at least as good as the Patch method with drastic reduction in storage and computational complexity.
使用压缩感知的纹理分类
本文提出了一种简单、新颖、功能强大的基于压缩感知和词袋模型的纹理分类方法,适用于在未知视点和光照下获取图像的大型纹理数据库应用。在特征提取阶段,从局部图像补丁中提取一小组随机特征。将随机特征嵌入到词袋模型中进行纹理分类。随机特征提取优于许多传统的特征提取方法,尽管他们的设计和复杂性。我们在CUReT数据库上进行了大量的实验来评估所提出方法的性能。结果表明,只要特征空间的维数在一定阈值以上,使用少量随机特征的方法可以获得优异的性能。我们的方法与最近最先进的方法进行了比较:Patch方法(Varma and Zisserman, TPAMI 09), MR8滤波器组方法(Varma and Zisserman, IJCV 05)和LBP方法(Ojala et al., TPAMI 02)。结果表明,该方法显著优于MR8和LBP,至少与Patch方法一样好,存储空间和计算复杂度大幅降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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