Abstract
Learning concepts from examples presented in user's query and infer the other items that belong to this query is still a significant challenge for images retrieval systems. Existing models from cognitive science namely Bayesian models of generalization mainly focus on this challenge where they remarkably succeed at explaining how to generalize from few examples in a wide range of domains. However their success largely depends on the validity of examples. They require that each example is a good representative, which is not always the case in the context of images retrieval. In this paper, we will extend the Bayesian models of generalization to identify the appropriate level of generalization for a given query in the context of query by semantic example systems. Our model uses an ontology as the basis of its hypothesis space which allows us to take advantages of its semantic richness and inference capacity. Experimental study using the ImageNet benchmark verifies the efficiency of our model in comparison to the state-of-the-art models of generalization.