Understanding user intention in image retrieval using multiple concept hierarchies
Image retrieval is the technique that helps Users to find and retrieve desired images from a large image dataset. The user has firstly to formulate a query that expresses his/her needs. This query may come in textual form as in semantic retrieval, in visual example form as in query by visual example, or as a combination of these two forms named query by semantic example. The focus of this paper lies in the techniques of analysing queries composed of multiple semantic examples. This is a very challenging task, to solve such a problem, we introduce a model based on Bayesian generalization. In cognitive science, Bayesian generalization, which is the base of most works in literature, is a method that tries to find, in one hierarchy of concepts, the parent concept of a given set of concepts. In addition and instead of using one single concept hierarchy, we propose a generalization so it can be used with multiple hierarchies where each one has a different semantic context and contains several abstraction levels. Experimental evaluations demonstrate that our method, which uses multiple hierarchies, yields better results than those using only one single hierarchy.
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