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Recent Advances in Intelligent Image Search and Video Retrieval

  • Chengjun Liu

Part of the Intelligent Systems Reference Library book series (ISRL, volume 121)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Qingfeng Liu, Yukhe Lavinia, Abhishek Verma, Joyoung Lee, Lazar Spasovic, Chengjun Liu
    Pages 1-19
  3. Ajit Puthenputhussery, Shuo Chen, Joyoung Lee, Lazar Spasovic, Chengjun Liu
    Pages 21-43
  4. Qingfeng Liu, Chengjun Liu
    Pages 45-63
  5. Ajit Puthenputhussery, Chengjun Liu
    Pages 91-114
  6. Emad Sami Jaha, Mark S. Nixon
    Pages 167-211
  7. Kitae Kim, Slobodan Gutesa, Branislav Dimitrijevic, Joyoung Lee, Lazar Spasovic, Wasif Mirza et al.
    Pages 213-231
  8. Back Matter
    Pages 233-235

About these proceedings

Introduction

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.

Keywords

Intelligent Techniques Image Search, Video Retrieval Smart City Internet of Things Scale-Invariant Feature Transform (SIFT) Pyramid Histogram of Oriented Gradients (PHOG) Local Binary Pattern (LBP) Spatial Pyramid Matching (SPM) Support Vector Machine (SVM)

Editors and affiliations

  • Chengjun Liu
    • 1
  1. 1.Department of Computer ScienceNew Jersey Institute of TechnologyUniversity Heights, NewarkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-52081-0
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-52080-3
  • Online ISBN 978-3-319-52081-0
  • Series Print ISSN 1868-4394
  • Series Online ISSN 1868-4408
  • Buy this book on publisher's site
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