Feature representation and extraction for image search and video retrieval

Qingfeng Liu, Yukhe Lavinia, Abhishek Verma, Joyoung Lee, Lazar Spasovic, Chengjun Liu

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationIntelligent Systems Reference Library
PublisherSpringer Science and Business Media Deutschland GmbH
Pages1-19
Number of pages19
DOIs
StatePublished - Jan 1 2017

Publication series

NameIntelligent Systems Reference Library
Volume121
ISSN (Print)1868-4394
ISSN (Electronic)1868-4408

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Information Systems and Management
  • Library and Information Sciences

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