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Erschienen in:
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2014 | OriginalPaper | Buchkapitel

3. Self-adaptation in Image and Video Retrieval

verfasst von : Paisarn Muneesawang, Ning Zhang, Ling Guan

Erschienen in: Multimedia Database Retrieval

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Kernel-Based Adaptive Image Retrieval Methods
Nächstes Kapitel Interactive Mobile Visual Search and Recommendation at Internet Scale
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Metadaten
Titel
Self-adaptation in Image and Video Retrieval
verfasst von
Paisarn Muneesawang
Ning Zhang
Ling Guan
Copyright-Jahr
2014
Buch
Multimedia Database Retrieval

Print ISBN: 978-3-319-11781-2

Electronic ISBN: 978-3-319-11782-9

Copyright-Jahr: 2014

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

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