Texture CLassification Using Compressed Sensing
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.