Sorted random projections for robust rotation-invariant texture classification
Pattern Recognition
Face segmentation using projection pursuit for texture classification
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Real-time compressive tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Gated boltzmann machine in texture modeling
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Real-time visual tracking based on an appearance model and a motion mode
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Information Sciences: an International Journal
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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.