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Toward semantic content-based image retrieval using Dempster–Shafer theory in multi-label classification framework

Toward semantic content-based image retrieval using Dempster–Shafer theory in multi-label... The gap between human semantic perception of an image and its abstraction by some low-level features is one of the main shortcomings of the actual content-based image retrieval (CBIR) systems. This paper presents an effort to overcome this drawback and proposes a CBIR approach in which retrieved images are more likely to satisfy the user expectations. Multi-label classification, in which an instance is assigned to different classes, provides a general framework to establish semantic correspondence between images in a database and a query image. Accordingly, in the framework of multi-label classification, a new feature fusion strategy is developed based on Dempster–Shafer evidence theory to allow handling lack of prior probabilities and uncertain information provided by low-level features. In this study, texture features are extracted through wavelet correlogram and color features are obtained using correlogram of vector quantized image colors. These features are subsequently fused via a possibility approach for being used in multi-label classification to retrieve images relevant to a query image. Experimental results on three well-known public and international image datasets demonstrate the superiority of the proposed algorithm over its close counterparts in terms of average precision and F1 measure. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

Toward semantic content-based image retrieval using Dempster–Shafer theory in multi-label classification framework

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer-Verlag London Ltd.
Subject
Computer Science; Multimedia Information Systems; Information Storage and Retrieval; Information Systems Applications (incl.Internet); Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Computer Science, general
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-017-0134-y
Publisher site
See Article on Publisher Site

Abstract

The gap between human semantic perception of an image and its abstraction by some low-level features is one of the main shortcomings of the actual content-based image retrieval (CBIR) systems. This paper presents an effort to overcome this drawback and proposes a CBIR approach in which retrieved images are more likely to satisfy the user expectations. Multi-label classification, in which an instance is assigned to different classes, provides a general framework to establish semantic correspondence between images in a database and a query image. Accordingly, in the framework of multi-label classification, a new feature fusion strategy is developed based on Dempster–Shafer evidence theory to allow handling lack of prior probabilities and uncertain information provided by low-level features. In this study, texture features are extracted through wavelet correlogram and color features are obtained using correlogram of vector quantized image colors. These features are subsequently fused via a possibility approach for being used in multi-label classification to retrieve images relevant to a query image. Experimental results on three well-known public and international image datasets demonstrate the superiority of the proposed algorithm over its close counterparts in terms of average precision and F1 measure.

Journal

International Journal of Multimedia Information Retrieval – Springer Journals

Published: Sep 20, 2017

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