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Learning and Recognition Methods 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

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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Authors and Affiliations

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

    Ajit Puthenputhussery, Joyoung Lee, Lazar Spasovic & Chengjun Liu

  2. The Neat Company, Philadelphia, PA, 19103, USA

    Shuo Chen

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  1. Ajit Puthenputhussery
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  2. Shuo Chen
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  3. Joyoung Lee
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  4. Lazar Spasovic
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  5. Chengjun Liu
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Correspondence to Ajit Puthenputhussery , Shuo Chen or Chengjun Liu .

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

    Chengjun Liu

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Puthenputhussery, A., Chen, S., Lee, J., Spasovic, L., Liu, C. (2017). Learning and Recognition Methods 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_2

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