Modeling user preferences in content-based image retrieval: A novel attempt to bridge the semantic gap

• A novel method for image retrieval have been proposed based on Generalized Linear Model.
• The model aims to bridge the semantic gap between low level features and user preferences.
• A drastic dimension reduction of feature vector is achieved by using a distance matrix.
• A broad set of experiments has been carried out for different databases.
• A new evaluation procedure has been proposed based on the empirical commutative distribution functions of the relevant and non-relevant retrieved images.

This paper is concerned with content-based image retrieval from a stochastic point of view. The semantic gap problem is addressed in two ways. First, a dimensional reduction is applied using the (pre-calculated) distances among images. The dimension of the reduced vector is the number of preferences that we allow the user to choose from, in this case, three levels. Second, the conditional probability distribution of the random user preference, given this reduced feature vector, is modeled using a proportional odds model. A new model is fitted at each iteration. The score used to rank the image database is based on the estimated probability function of the random preference. Additionally, some memory is incorporated in the procedure by weighting the current and previous scores. Also, a novel evaluation procedure is proposed in this work based on the empirical commutative distribution functions of the relevant and non-relevant retrieved images. Good experimental results are achieved in very different experimental setups and tested in different databases.