Learning and recognition methods for image search and video retrieval

Ajit Puthenputhussery, Shuo Chen, Joyoung Lee, Lazar Spasovic, Chengjun Liu

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

Effective learning and recognitionmethods play an important role in intelligent image search and video retrieval. This chapter therefore reviews some popular learning and recognitionmethods that are broadly applied for image search and video retrieval. First some popular deep learning methods are discussed, such as the feedforward deep neural networks, the deep autoencoders, the convolutional neural networks, and the Deep Boltzmann Machine (DBM). Second, Support Vector Machine (SVM), which is one of the popular machine learning methods, is reviewed. In particular, the linear support vector machine, the soft-margin support vector machine, the non-linear support vector machine, the simplified support vector machine, the efficient Support Vector Machine (eSVM), and the applications of SVM to image search and video retrieval are discussed. Finally, other popular kernel methods and new similarity measures are briefly reviewed.

Original languageEnglish (US)
Title of host publicationIntelligent Systems Reference Library
PublisherSpringer Science and Business Media Deutschland GmbH
Pages21-43
Number of pages23
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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