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Feature Representation and Extraction for Image Search and Video Retrieval

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Recent Advances in Intelligent Image Search and Video Retrieval

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

Abstract

The ever-increasing popularity of intelligent image search and video retrieval warrants a comprehensive study of the major feature representation and extraction methods often applied in image search and video retrieval. Towards that end, this chapter reviews some representative feature representation and extraction approaches, such as the Spatial Pyramid Matching (SPM) , the soft assignment coding, the Fisher vector coding , the sparse coding and its variants, the Local Binary Pattern (LBP) , the Feature Local Binary Patterns (FLBP) , the Local Quaternary Patterns (LQP), the Feature Local Quaternary Patterns (FLQP) , the Scale-invariant feature transform (SIFT) , and the SIFT variants, which are broadly applied in intelligent image search and video retrieval .

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Author information

Authors and Affiliations

  1. New Jersey Institute of Technology, Newark, NJ, 07102, USA

    Qingfeng Liu, Joyoung Lee, Lazar Spasovic & Chengjun Liu

  2. California State University, Fullerton, CA, 92834, USA

    Yukhe Lavinia & Abhishek Verma

Authors
  1. Qingfeng Liu
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  2. Yukhe Lavinia
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  3. Abhishek Verma
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  4. Joyoung Lee
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  5. Lazar Spasovic
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  6. Chengjun Liu
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Corresponding authors

Correspondence to Qingfeng Liu , Yukhe Lavinia , Abhishek Verma or Chengjun Liu .

Editor information

Editors and Affiliations

  1. Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, New Jersey, USA

    Chengjun Liu

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Liu, Q., Lavinia, Y., Verma, A., Lee, J., Spasovic, L., Liu, C. (2017). Feature Representation and Extraction for Image Search and Video Retrieval. In: Liu, C. (eds) Recent Advances in Intelligent Image Search and Video Retrieval. Intelligent Systems Reference Library, vol 121 . Springer, Cham. https://doi.org/10.1007/978-3-319-52081-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-52081-0_1

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