Sorted Random Projections for robust texture classification

  • Authors:

  • Li Liu;Paul Fieguth;Gangyao Kuang; Hongbin Zha

  • Affiliations:

  • National University of Defense Technology, Changsha, Hunan, China 410043;System Design Engineering Dept., University of Waterloo, Ontario, Canada N2L 3G1;National University of Defense Technology, Changsha, Hunan, China 410043;Machine Intelligence Dept., Peking University, Beijing, China 100871

  • Venue:
  • ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
  • Year:
  • 2011

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Abstract

This paper presents a simple and highly effective system for robust texture classification, based on (1) random local features, (2) a simple global Bag-of-Words (BoW) representation, 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 different texture image representations (histograms and signatures) 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 outperforms the state-of-the-art descriptors in material categorization.