Sparse Representation, Modeling and Learning in Visual Recognition: Theory, Algorithms and Applications (Advances in Computer Vision and Pattern Recognition)
暫譯: 視覺識別中的稀疏表示、建模與學習：理論、演算法與應用（計算機視覺與模式識別進展）

This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.