A New Approach to Interactive Visual Search with RBF Networks Based on Preference Modelling

In this paper we propose a new method for image retrieval with relevance feedback based on eliciting preferences from the decision-maker acquiring visual information from an image database. The proposed extension of the common approach to image retrieval with relevance feedback allows it to be applied to objects with non-homogenous colour and texture. This has been accomplished by the algorithms, which model user queries by an RBF neural network. As an example of application of this approach, we have used a content-based search in an atlas of species. An experimental comparison with the commonly used content-based image retrieval approach is presented.