Issue No. 03 - March (2012 vol. 34)
Li Liu , National University of Defense Technology, Changsha
Paul W. Fieguth , University of Waterloo, Waterloo
Inspired by theories of sparse representation and compressed sensing, this paper presents a simple, novel, yet very powerful approach for texture classification based on random projection, suitable for large texture database applications. 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 a 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 have conducted extensive experiments on each of the CUReT, the Brodatz, and the MSRC databases, comparing the proposed approach to four state-of-the-art texture classification methods: Patch, Patch-MRF, MR8, and LBP. We show that our approach leads to significant improvements in classification accuracy and reductions in feature dimensionality.
Texture classification, random projections, sparse representation, compressed sensing, textons, image patches, bag of words.
L. Liu and P. W. Fieguth, "Texture Classification from Random Features," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 574-586, 2011.