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2017 | OriginalPaper | Buchkapitel

1. Feature Representation and Extraction for Image Search and Video Retrieval

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

Erschienen in: Recent Advances in Intelligent Image Search and Video Retrieval

Verlag: Springer International Publishing

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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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Nächstes Kapitel Learning and Recognition Methods for Image Search and Video Retrieval
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Metadaten
Titel
Feature Representation and Extraction for Image Search and Video Retrieval
verfasst von
Qingfeng Liu
Yukhe Lavinia
Abhishek Verma
Joyoung Lee
Lazar Spasovic
Chengjun Liu
Copyright-Jahr
2017
Buch
Recent Advances in Intelligent Image Search and Video Retrieval

Print ISBN: 978-3-319-52080-3

Electronic ISBN: 978-3-319-52081-0

Copyright-Jahr: 2017

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