Sorted random projections for robust texture classification

This paper presents a simple and highly effective system for robust texture classification, based on (1) random lo-cal features, (2) a simple global Bag-of-Words (BoW) rep-resentation, and (3) Support Vector Machines (SVMs) based classification. The key contribution in this work is to apply a sorting strategy to a universal yet information-preserving random projection (RP) technique, then comparing two dif-ferent texture image representations (histograms and signa-tures) with various kernels in the SVMs. We have tested our texture classification system on six popular and challenging texture databases for exemplar based texture classification, comparing with 12 recent state-of-the-art methods. Experimental results show that our texture classification system yields the best classification rates of which we are aware of 99.37 % for CUReT, 97.16% for Brodatz, 99.30 % for UMD and 99.29 % for KTH-TIPS. Moreover, combining random features significantly outper-forms the state-of-the-art descriptors in material catego-rization. 1