关键词: 稀疏编码, 流形结构, 基学习, 标签相关性, 共同特征表达

Abstract: In order to overcome the difference of features between different image domains and the gap of distribution, a learning algorithm based on co-regularized sparse concept encoding is proposed in this paper. Firstly, the distribution difference and the label consistency information of image datasets are incorporated into the sparse coding model to study the robust sparse representation of the cross-domain image. Then, the low-dimensional manifold structure is excavated from the high-dimentional image feature space to form vector set, which contructs transfer sparse coding for robust image representation. The method captures the commonality underlying of the different image dataset and realizes cross-domain transfer for image tags. The experiment shows that the method achieves more robust feature representation, and its classification performance is significantly better than other related methods.

Key words: sparse coding, manifold structure, basis learning, label relevance, common feature expression