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1 October 2020 Effective image retrieval method of natural images in a large database using fuzzy class membership
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Abstract

We describe the improvements of the content-based image retrieval (CBIR) system using a fuzzy class membership for the natural-color images. The fuzzy class membership-based retrieval (CMR) framework has shown promising improvements on texture databases by exploiting confidence in classification using a multilayer perceptron (MLP). CMR is known to improve the average precision of retrieval along with modest variance, and the framework is not restricted to any particular feature set. However, their efficacy is not known for natural colored images. In the proposed approach, we have added a new classifier, radial basis function network, in place of MLP in the CMR framework. We show a way to adapt a new classifier in the fuzzy CMR framework. Comparison with state-of-the-art CBIR systems shows that the proposed modifications have an edge over its competition in terms of precision for four popular image databases: viz. Corel-1k, Corel-5k, Corel-10k, and CIFAR-10.

© 2020 SPIE and IS&T 1017-9909/2020/$28.00© 2020 SPIE and IS&T
Mandar Kale and Sudipta Mukhopadhyay "Effective image retrieval method of natural images in a large database using fuzzy class membership," Journal of Electronic Imaging 29(5), 053012 (1 October 2020). https://doi.org/10.1117/1.JEI.29.5.053012
Received: 20 February 2020; Accepted: 14 September 2020; Published: 1 October 2020
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