Machine Learning and Statistical Modeling Approaches to Image Retrieval

  • Yixin Chen
  • Jia Li
  • James Z. Wang

Part of the The Information Retrieval Series book series (INRE, volume 14)

About this book

Introduction

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.

Keywords

Hidden Markov Model LED Performance algorithms classification clustering complexity fuzzy fuzzy systems learning machine learning modeling organization

Authors and affiliations

  • Yixin Chen
    • 1
  • Jia Li
    • 2
  • James Z. Wang
    • 2
  1. 1.The Research Institute for ChildrenUniversity of New OrleansNew OrleansUSA
  2. 2.The Pennsylvania State UniversityUniversity ParkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/b116104
  • Copyright Information Kluwer Academic Publishers 2004
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4020-8034-0
  • Online ISBN 978-1-4020-8035-7
  • Series Print ISSN 1387-5264
  • About this book
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