Multi-Level Feature Descriptor for Robust Texture Classification via Locality-Constrained Collaborative Strategy
This work addresses texture classification challenges for computer vision applications, but it is incremental as it builds on existing spatial pyramid matching and collaborative representation methods.
The paper tackles robust texture classification under translation, scale, and viewpoint changes by proposing a multi-level feature descriptor with a locality-constrained collaborative strategy, achieving competitive or superior performance on three public datasets, especially with few training samples.
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.