2012/03/02 by Shu Kong, Donghui Wang, Kong, Shu +1
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #Remote-Sensing Image Classification #cs.CV #cs.IR
paper · pdf · doi:10.48550/arxiv.1203.0488
arxiv created 2012/03/02 · openalex publication_date 2012/03/02 · arxiv updated 2012/03/06 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.