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Machine Vision and Applications

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01.08.2016 | Original Paper

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

verfasst von: Asma ElAdel, Ridha Ejbali, Mourad Zaied, Chokri Ben Amar

Erschienen in: Machine Vision and Applications | Ausgabe 6/2016

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Abstract

Content-based image retrieval (CBIR) has been a worthwhile topic for research for several years. A powerful CBIR system should minimize the semantic gap between the low-level features and high-level concepts in order to satisfy users requirements. Moreover, it should take into consideration the execution time. In this paper, we present a new semantic approach for CBIR supported by a parallel aggregation of content-based features extraction (shape, texture, color) using fuzzy support decision mechanisms. Shape features are based on Fast Beta Wavelet Network modeling and Hue moments. The texture descriptor is based on Energy computing at different decomposition levels. Finally, we present an implementation of a new color feature extraction based on fuzzy indexed color map. In the second stage, we propose a Fuzzy Decision Support System for feature (shape, texture, color) aggregation to improve the retrieval performance. The proposed approach is tested on four most popular datasets: Wang, INRIA Holidays, UKBench and samples from ImageNet, and the experiments showed that the proposed approach can achieve a satisfactory retrieval performance with an acceptable search time.

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Available at: http://wang.ist.psu.edu/docs/related/.

Available at: http://lear.inrialpes.fr/~jegou/data.php#holidays.

Available at: http://www.vis.uky.edu/~stewe/ukbench/.

Available at: http://www.image-net.org/.

http://www.image-net.org/synset?wnid=n02084071.

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Titel: A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system
verfasst von: Asma ElAdel
Ridha Ejbali
Mourad Zaied
Chokri Ben Amar
Publikationsdatum: 01.08.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Machine Vision and Applications / Ausgabe 6/2016
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-016-0789-z

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