Description
Book SynopsisThis book provides a broader introduction to the theories and applications of sparse coding techniques in computer vision research. It introduces sparse coding in the context of representation learning, illustrates the fundamental concepts, and summarizes the most active research directions. A variety of applications of sparse coding are discussed, ranging from low-level image processing tasks such as super-resolution and de-blurring to high-level semantic understanding tasks such as image recognition, clustering and fusion.The book is suitable to be used as an introductory overview to this field, with its theoretical part being both easy and precious enough for quick understanding. It is also of great value to experienced researchers as it offers new perspective to the underlying mechanism of sparse coding, and points out potential future directions for different applications.
Table of ContentsSparse Priors; Sparse Solvers; Dictionary Learning Algorithms; Image Super-Resolution with Coupled Dictionaries; Image De-Blurring with Sparse Regularization; Sensor Fusion with Joint Structured Sparsity; Clustering with L1-Graph; Discriminative Representation Learning; Hyper-Spectral Image Classification;
