  This paper introduces a simple but highly efficient ensemble for robust
texture classification, which can effectively deal with translation, scale and
changes of significant viewpoint problems. The proposed method first inherits
the spirit of spatial pyramid matching model (SPM), which is popular for
encoding spatial distribution of local features, but in a flexible way,
partitioning the original image into different levels and incorporating
different overlapping patterns of each level. This flexible setup helps capture
the informative features and produces sufficient local feature codes by some
well-chosen aggregation statistics or pooling operations within each
partitioned region, even when only a few sample images are available for
training. Then each texture image is represented by several orderless feature
codes and thereby all the training data form a reliable feature pond. Finally,
to take full advantage of this feature pond, we develop a collaborative
representation-based strategy with locality constraint (LC-CRC) for the final
classification, and experimental results on three well-known public texture
datasets demonstrate the proposed approach is very competitive and even
outperforms several state-of-the-art methods. Particularly, when only a few
samples of each category are available for training, our approach still
achieves very high classification performance.
