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Visual Pattern Discovery and Recognition

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 analyti...

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Bibliographic Details
Main Authors: Wang, Hongxing, Weng, Chaoqun (Author), Yuan, Junsong (Author)
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2017, 2017
Edition:1st ed. 2017
Series:SpringerBriefs in Computer Science
Subjects:
Computer Vision
Data Mining
Data Mining And Knowledge Discovery
Automated Pattern Recognition
Pattern Recognition Systems
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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505 0 |a 1. Introduction -- 2. Context-Aware Discovery of Visual Co-occurrence Patterns -- 3. Hierarchical Sparse Coding for Visual Co-occurrence Discovery -- 4. Feature Co-occurrence for Visual Labeling -- 5. Visual Clustering with Minimax Feature Fusion -- 6. Conclusion 
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520 |a 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 

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