The Information Retrieval Series

Machine Learning and Statistical Modeling Approaches to Image Retrieval

Authors: Chen, Yixin, Li, Jia, Wang, James Z.

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About this book

In the early 1990s, the establishment of the Internet brought forth a revolutionary viewpoint of information storage, distribution, and processing: the World Wide Web is becoming an enormous and expanding distributed digital library. Along with the development of the Web, image indexing and retrieval have grown into research areas sharing a vision of intelligent agents. Far beyond Web searching, image indexing and retrieval can potentially be applied to many other areas, including biomedicine, space science, biometric identification, digital libraries, the military, education, commerce, culture and entertainment.
Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results. The topics of this book reflect authors' experiences of machine learning and statistical modeling based image indexing and retrieval. This book contains detailed references for further reading and research in this field as well.

Table of contents (9 chapters)

Buy this book

eBook $139.00
price for USA (gross)
  • ISBN 978-1-4020-8035-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $189.00
price for USA
  • ISBN 978-1-4020-8034-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $179.00
price for USA
  • ISBN 978-1-4757-7930-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning and Statistical Modeling Approaches to Image Retrieval
Authors
Series Title
The Information Retrieval Series
Series Volume
14
Copyright
2004
Publisher
Springer US
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4020-8035-7
DOI
10.1007/b116104
Hardcover ISBN
978-1-4020-8034-0
Softcover ISBN
978-1-4757-7930-1
Series ISSN
1387-5264
Edition Number
1
Number of Pages
XVII, 182
Topics