Ennekhli, M. and El Ouazzani, R. and El Haziti, M. (2018) A new content-based image retrieval system using Parzen relevance feedback and Kullback-Leibler divergence. International Journal of Computer Information Systems and Industrial Management Applications, 10. 018-027.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

In content-based image retrieval (CBIR), many multimedia applications use visual distance to find a collection of images which share the same properties. However, visual distance between two images is often not suitable to semantic distance between the same images. In fact, the semantics term refers to the way how people interpret the image content. Currently, it is difficult to find good correspondences between high-level image semantics and low-level image features which create a "semantic gap". In this paper, we propose a new relevance feedback method which reduces the semantic gap be- tween images. The key steps of our process are the following: At first, we compute the visual distance through the Kullback-Leibler Divergence (KLD). Then, we apply the Relevance Feed- back to enhance the retrieval effectiveness by using three different machine learning algorithms: Gaussian Mixture Model (GMM), Support Vector Machines (SVM) and Parzen classifiers; thus, we learn relevant and irrelevant images according to user selection. Experimental results on 5000 images from the COREL database show that comparing to traditional approaches, Parzen classifier is effective and can significantly improve retrieval rates. © MIR Labs.

Item Type: Article
Subjects: Business, Management and Accounting
Divisions: SCIENTIFIC PRODUCTION > Business, Management and Accounting
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:44
URI: http://eprints.umi.ac.ma/id/eprint/1663

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