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Content-Based Image Retrieval approaches have been marked by the semantic gap (inconsistency) between the perception of the user and the visual description of the image. This inconsistency is often linked to the use of predefined visual features randomly selected and applied whatever the application domain. In this paper we propose an approach that adapts the selection of visual features to semantic content ensuring the coherence between them. We first design visual and semantic descriptive ontologies. These ontologies are then explored by association rules aiming to link semantic descriptor (a concept) to a set of visual features. The obtained feature collections are selected according to the annotated query images. Different strategies have been experimented and their results have shown an improvement of the retrieval task based on relevant feature selections.