Abstract
In this paper, the Relevance Feedback procedure for Content Based Image Retrieval is considered as an Exploration-Exploitation approach. The proposed method exploits the information obtained from the relevance score as computed by a Nearest Neighbor approach in the exploitation step. The idea behind the Nearest Neighbor relevance feedback is to retrieve the immediate neighborhood of the area of the feature space where relevant images are found. The exploitation step aims at returning to the user the maximum number of relevant images in a local region of the feature space. On the other hand, the exploration step aims at driving the search towards different areas of the feature space in order to discover not only relevant images but also informative images. Similar ideas have been proposed with Support Vector Machines, where the choice of the informative images has been driven by the closeness to the decision boundary. Here, we propose a rather simple method to explore the representation space in order to present to the user a wider variety of images. Reported results show that the proposed technique allows to improve the performance in terms of average precision and that the improvements are higher if compared to techniques that use an SVM approach.
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Piras, L., Giacinto, G., Paredes, R. (2012). Enhancing Image Retrieval by an Exploration-Exploitation Approach. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_28
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