Preference Extraction in Image Retrieval

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Author(s): Pawel Rotter (European Commission, Joint Research Centre, Institute for Prospective Technological Studies, Spain & AGH-University of Science and Technology, Poland)and Andrzej M.J. Skulimowski (AGH-University of Science and Technology, Poland)
Copyright: 2009
Pages: 26
Source title: Artificial Intelligence for Maximizing Content Based Image Retrieval
Source Author(s)/Editor(s): Zongmin Ma (Northeastern University, China)
DOI: 10.4018/978-1-60566-174-2.ch011

Keywords: Artificial Intelligence / Computer Science & IT / Computer Vision & Image Processing / Information Science Reference

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Abstract

In this chapter, we describe two new approaches to content-based image retrieval (CBIR) based on preference information provided by the user interacting with an image search system. First, we present the existing methods of image retrieval with relevance feedback, which serve then as a reference for the new approaches. The first extension of the distance function-based CBIR approach makes it possible to apply this approach to complex objects. The new algorithm is based on an approximation of user preferences by a neural network. Further, we propose another approach to image retrieval, which uses reference sets to facilitate image comparisons. The methods proposed have been implemented, and compared with each other, and with the earlier approaches. Computational experiments have proven that the new preference extraction and image retrieval procedures here proposed are numerically efficient. Finally, we provide a real-life illustration of the methods proposed: an image-based hotel selection procedure.

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