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This paper introduces a simple but highly efficient ensemble for robusttexture classification, which can effectively deal with translation, scale andchanges of significant viewpoint problems. The proposed method first inheritsthe spirit of spatial pyramid matching model (SPM), which is popular forencoding spatial distribution of local features, but in a flexible way,partitioning the original image into different levels and incorporatingdifferent overlapping patterns of each level. This flexible setup helps capturethe informative features and produces sufficient local feature codes by somewell-chosen aggregation statistics or pooling operations within eachpartitioned region, even when only a few sample images are available fortraining. Then each texture image is represented by several orderless featurecodes and thereby all the training data form a reliable feature pond. Finally,to take full advantage of this feature pond, we develop a collaborativerepresentation-based strategy with locality constraint (LC-CRC) for the finalclassification, and experimental results on three well-known public texturedatasets demonstrate the proposed approach is very competitive and evenoutperforms several state-of-the-art methods. Particularly, when only a fewsamples of each category are available for training, our approach stillachieves very high classification performance.

Multi-Level Feature Descriptor for Robust Texture Classification via Locality-Constrained Collaborative Strategy