Compressed Sensing for Robust Texture Classification

This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing. At the feature extraction stage, a small set of random features is extracted from local image patches. The random features are embedded into a bag-of-words model to perform texture classification, thus learning and classification are carried out in the compressed domain. The proposed unconventional random feature extraction is simple, yet by leveraging the sparse nature of texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We report extensive experiments comparing the proposed method to the state-of-the-art in texture classification on four databases: CUReT, Brodatz, UIUC and KTH-TIPS. Our approach leads to significant improvements in classification accuracy and reductions in feature dimensionality, exceeding the best reported results on CUReT, Brodatz and KTH-TIPS.