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Existing image retrieval frameworks use the low-level visual features extracted from images to learn a set of semantic categories and tend to discard the conceptual properties of these features. In this paper, we propose to use the conceptual information of the low-level image features to establish a correspondence between the image features and a set of high-level semantic categories. In doing so, image objects are indexed using concepts that describe the low-level color, texture, and shape features in a way that reflects the human understanding of their visual properties. Experimentally, we show that using the conceptual information outperforms using the low-level features for indexing and classifying image regions. In the retrieval process, we demonstrate the effectiveness of using the conceptual information in queries to enrich the search process of users by handling non-trivial queries incorporating both the semantic concepts and the conceptual information of the queried semantic concepts.