Sparse Representation, Modeling and Learning in Visual Recognition: Theory, Algorithms and Applications (Advances in Computer Vision and Pattern Recognition)

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.

這本獨特的文獻/參考書全面回顧了稀疏表示、建模和學習的最新技術。本書既探討了理論基礎，也詳細介紹了算法實現的細節，並突出了壓縮感知研究在視覺識別和計算機視覺中的實際應用。主題和特點包括：描述了稀疏恢復方法、強健且高效的稀疏表示以及大規模視覺識別；涵蓋了特徵表示和學習、稀疏引發的相似性以及基於稀疏表示和學習的分類器；討論了低秩矩陣近似、壓縮感知中的圖模型、基於協作表示的分類以及高維非線性學習；附錄中提供了額外的計算機編程資源，並解釋了理解本書所需的基本數學知識。