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Self-adaptation in Image and Video Retrieval

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Multimedia Database Retrieval

Part of the book series: Multimedia Systems and Applications ((MMSA))

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Abstract

This chapter explores the automatic methods for implementing pseudo-relevance feedback for retrieval of images and videos. The automation is based on dynamic self-organization, the self-organizing tree map that is capable of identification of relevance in place of human users. The automation process leads to the avoidance of errors in excessive human involvement, and enlarging the size of training set, as compared to traditional relevance feedback. The automatic retrieval system applies for image retrieval in compressed domains (i.e., JPEG and wavelet based coders). In addition, the system incorporates knowledge-based learning to acquire a suitable weighting scheme for unsupervised relevance identification. In the video domain, the pseudo-relevance feedback is implemented by an adaptive cosine network than enhances retrieval accuracy through the network’s forward–backward signal propagation, without user input.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Naresuan University, Muang, Phitsanulok, Thailand

    Paisarn Muneesawang

  2. Department of Electrical and Computing Engineering, Ryerson University, Toronto, ON, Canada

    Ning Zhang

  3. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Ling Guan

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  1. Paisarn Muneesawang
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  2. Ning Zhang
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  3. Ling Guan
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© 2014 Springer International Publishing Switzerland

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Muneesawang, P., Zhang, N., Guan, L. (2014). Self-adaptation in Image and Video Retrieval. In: Multimedia Database Retrieval. Multimedia Systems and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-11782-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-11782-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11781-2

  • Online ISBN: 978-3-319-11782-9

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