Sparse representation, modeling and learning in visual recognition : theory, algorithms and applications
著者
書誌事項
Sparse representation, modeling and learning in visual recognition : theory, algorithms and applications
(Advances in computer vision and pattern recognition / Sameer Singh, Sing Bing Kang, series editors)
Springer, c2015
大学図書館所蔵 全5件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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
「Nielsen BookData」 より