@inproceedings {209,
	title = {Sorted Random Projections for Robust Texture Classification},
	booktitle = {International Conference on Computer Vision (ICCV)},
	year = {2011},
	address = {Barcelona},
	abstract = {<p>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.</p>

<p>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.</p>
},
	author = {L Liu and P Fieugth and G Kuang and H Zha}
}
