Editors:
- Presents advanced pattern recognition and machine learning methods using sparse representation and
- Introduces innovative similarity
- Addresses both theory and practice
- Includes supplementary material: sn.pub/extras
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 121)
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Table of contents (9 papers)
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Front Matter
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Back Matter
About this book
This book initially reviews the major feature representation and extraction methods and effective learning and recognition approaches, which have broad applications in the context of intelligent image search and video retrieval. It subsequently presents novel methods, such as improved soft assignment coding, Inheritable Color Space (InCS) and the Generalized InCS framework, the sparse kernel manifold learner method, the efficient Support Vector Machine (eSVM), and the Scale-Invariant Feature Transform (SIFT) features in multiple color spaces. Lastly, the book presents clothing analysis for subject identification and retrieval, and performance evaluation methods of video analytics for traffic monitoring.
Digital images and videos are proliferating at an amazing speed in the fields of science, engineering and technology, media and entertainment. With the huge accumulation of such data, keyword searches and manual annotation schemes may no longer be able to meet the practical demand for retrieving relevant content from images and videos, a challenge this book addresses.This book initially reviews the major feature representation and extraction methods and effective learning and recognition approaches, which have broad applications in the context of intelligent image search and video retrieval. It subsequently presents novel methods, such as improved soft assignment coding, Inheritable Color Space (InCS) and the Generalized InCS framework, the sparse kernel manifold learner method, the efficient Support Vector Machine (eSVM), and the Scale-Invariant Feature Transform (SIFT) features in multiple color spaces. Lastly, the book presents clothing analysis for subject identification and retrieval, and performance evaluation methods of video analytics for traffic monitoring.
Digital images and videos are proliferating at an amazing speed in the fields of science, engineering and technology, media and entertainment. With the huge accumulation of such data, keyword searches and manual annotation schemes may no longer be able to meet the practical demand for retrieving relevant content from images and videos, a challenge this book addresses.
Editors and Affiliations
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Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, USA
Chengjun Liu
Bibliographic Information
Book Title: Recent Advances in Intelligent Image Search and Video Retrieval
Editors: Chengjun Liu
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-319-52081-0
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-52080-3Published: 19 April 2017
Softcover ISBN: 978-3-319-84816-7Published: 08 May 2018
eBook ISBN: 978-3-319-52081-0Published: 18 April 2017
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XVII, 235
Number of Illustrations: 3 b/w illustrations, 85 illustrations in colour
Topics: Computational Intelligence, Image Processing and Computer Vision, Artificial Intelligence