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2017 | OriginalPaper | Buchkapitel

2. Learning and Recognition Methods for Image Search and Video Retrieval

verfasst von : Ajit Puthenputhussery, Shuo Chen, 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

Effective learning and recognition methods play an important role in intelligent image search and video retrieval. This chapter therefore reviews some popular learning and recognition methods 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.

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Vorheriges Kapitel Feature Representation and Extraction for Image Search and Video Retrieval

Nächstes Kapitel Improved Soft Assignment Coding for Image Classification

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Titel: Learning and Recognition Methods for Image Search and Video Retrieval
verfasst von: Ajit Puthenputhussery
Shuo Chen
Joyoung Lee
Lazar Spasovic
Chengjun Liu
Verlag: Springer International Publishing
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_2

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