Sparse representation, modeling and learning in visual recognition : theory, algorithms and applications

Author(s)

• Cheng, Hong

Bibliographic Information

Sparse representation, modeling and learning in visual recognition : theory, algorithms and applications

Hong Cheng

（Advances in computer vision and pattern recognition / Sameer Singh, Sing Bing Kang, series editors）

Springer, c2015

Note

Includes bibliographical references and index

Description and Table of Contents

Description

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.

Table of Contents

Part I: Introduction and Fundamentals Introduction The Fundamentals of Compressed Sensing Part II: Sparse Representation, Modeling and Learning Sparse Recovery Approaches Robust Sparse Representation, Modeling and Learning Efficient Sparse Representation and Modeling Part III: Visual Recognition Applications Feature Representation and Learning Sparsity Induced Similarity Sparse Representation and Learning Based Classifiers Part IV: Advanced Topics Beyond Sparsity Appendix A: Mathematics Appendix B: Computer Programming Resources for Sparse Recovery Approaches Appendix C: The source Code of Sparsity Induced Similarity Appendix D: Derivations

by "Nielsen BookData"

Details

• NCID: BB19368922
• ISBN:
  • 9781447167136
• Country Code: uk
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: London
• Pages/Volumes: xiv, 257 p.
• Size: 25 cm
• Classification:
  • DC23 : 006.37
• Subject Headings:
  • LCSH : Computer vision
  • LCSH : Artificial intelligence
• Parent Bibliography ID:
  • BB07744302