Relevance Feedback for Content-Based Image Retrieval Using Support Vector Machines and Feature Selection

Authors:
Apostolos Marakakis;Nikolaos Galatsanos;Aristidis Likas;Andreas Stafylopatis
Affiliations:
School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece 15780;Department of Electrical and Computer Engineering, University of Patras, Patras, Greece 26500;Department of Computer Science, University of Ioannina, Ioannina, Greece 45110;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece 15780
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

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Abstract

A relevance feedback (RF) approach for content-based image retrieval (CBIR) is proposed, which is based on Support Vector Machines (SVMs) and uses a feature selection technique to reduce the dimensionality of the image feature space. Specifically, each image is described by a multidimensional vector combining color, texture and shape information. In each RF round, the positive and negative examples provided by the user are used to determine a relatively small number of the most important features for the corresponding classification task, via a feature selection methodology. After the feature selection has been performed, an SVM classifier is trained to distinguish between relevant and irrelevant images according to the preferences of the user, using the restriction of the user examples on the set of selected features. The trained classifier is subsequently used to provide an updated ranking of the database images represented in the space of the selected features. Numerical experiments are presented that demonstrate the merits of the proposed relevance feedback methodology.