Sparse Representation, Modeling and Learning in Visual Recognition: Theory, Algorithms and Applications

Describes the latest research trends in compressed sensing, covering sparse representation, modeling and learning Examines sensing applications in visual recognition, including sparsity induced similarity, and sparse coding-based classifying frameworks Discusses in detail the theory and algorithms of compressed sensing 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. Topics Pattern Recognition Image Processing and Computer Vision Artificial Intelligence (incl. Robotics)