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Titlebook: Visual Pattern Discovery and Recognition; Hongxing Wang,Chaoqun Weng,Junsong Yuan Book 2017 The Author(s) 2017 Visual Pattern Discovery.Mu

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發(fā)表于 2025-3-21 19:12:19 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Visual Pattern Discovery and Recognition
編輯Hongxing Wang,Chaoqun Weng,Junsong Yuan
視頻videohttp://file.papertrans.cn/984/983761/983761.mp4
概述Enables readers to quickly access the world of visual pattern discovery and multi-feature fusion research.Offers detailed methodology to handle visual data with large variations in spatial and feature
叢書名稱SpringerBriefs in Computer Science
圖書封面Titlebook: Visual Pattern Discovery and Recognition;  Hongxing Wang,Chaoqun Weng,Junsong Yuan Book 2017 The Author(s) 2017 Visual Pattern Discovery.Mu
描述.This book presents a systematic study of visual pattern discovery, from unsupervised to semi-supervised manner approaches, and from dealing with a single feature to multiple types of features. Furthermore, it discusses the potential applications of discovering visual patterns for visual data analytics, including visual search, object and scene recognition..It is intended as a reference book for advanced undergraduates or postgraduate students who are interested in visual data analytics, enabling them to quickly access the research world and acquire a systematic methodology rather than a few isolated techniques to analyze visual data with large variations. It is also inspiring for researchers working in computer vision and pattern recognition fields. Basic knowledge of linear algebra, computer vision and pattern recognition would be helpful to readers..
出版日期Book 2017
關鍵詞Visual Pattern Discovery; Multi-feature Fusion; Context-Aware Discovery; Co-occurrence Pattern; Hierarch
版次1
doihttps://doi.org/10.1007/978-981-10-4840-1
isbn_softcover978-981-10-4839-5
isbn_ebook978-981-10-4840-1Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Author(s) 2017
The information of publication is updating

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Context-Aware Discovery of Visual Co-occurrence Patterns,t-aware clustering problem and propose a self-learning procedure to iteratively refine the result until it converges. By discovering both spatial co-occurrence patterns among visual primitives and feature co-occurrence patterns among different types of features, the proposed method can better address the ambiguities of visual primitives.
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Hierarchical Sparse Coding for Visual Co-occurrence Discovery,in spatial and feature spaces, which can be more representative compared with the low-level sparse codes of visual primitive features. The superior results on various tasks of visual categorization and pattern discovery validate the effectiveness of the proposed approach.
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