| 书目名称 | Sparse Representation, Modeling and Learning in Visual Recognition | | 副标题 | Theory, Algorithms a | | 编辑 | Hong Cheng | | 视频video | | | 概述 | Describes the latest research trends in compressed sensing, covering sparse representation, modeling and learning.Examines sensing applications in visual recognition, including sparsity induced simila | | 丛书名称 | Advances in Computer Vision and Pattern Recognition | | 图书封面 |  | | 描述 | 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. | | 出版日期 | Book 2015 | | 关键词 | Compressed Sensing; Dictionary Learning; Sparse Bayesian Learning; Sparse Coding; Sparse Representation; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4471-6714-3 | | isbn_softcover | 978-1-4471-7251-2 | | isbn_ebook | 978-1-4471-6714-3Series ISSN 2191-6586 Series E-ISSN 2191-6594 | | issn_series | 2191-6586 | | copyright | Springer-Verlag London 2015 |
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发表于 2025-3-21 18:38:08
