2019/11/23 by He-Feng Yin, Yin, He-Feng, Xiao‐Jun Wu +5
Computer Science · Engineering · #Blind Source Separation Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Sparse and Compressive Sensing Techniques
paper · doi:10.48550/arxiv.1911.10301
openalex publication_date 2019/11/23 · openalex created_date 2019/12/05 · openalex updated_date 2026/07/28
Sparse-representation-based classification (SRC) has been widely studied and developed for various practical signal classification applications. However, the performance of a SRC-based method is degraded when both the training and test data are corrupted. To counteract this problem, we propose an approach that learns Representation with Block-Diagonal Structure (RBDS) for robust image recognition. To be more specific, we first introduce a regularization term that captures the block-diagonal structure of the target representation matrix of the training data. The resulting problem is then solved by an optimizer. Last, based on the learned representation, a simple yet effective linear classifier is used for the classification task. The experimental results obtained on several benchmarking datasets demonstrate the efficacy of the proposed RBDS method.