Region-Based Image Retrieval with High-Level Semantic Color Names

Performance of traditional content-based image retrieval systems is far from user’s expectation due to the ‘semantic gap’ between low-level visual features and the richness of human semantics. In attempt to reduce the ‘semantic gap’, this paper introduces a region-based image retrieval system with high-level semantic color names. In this system, database images are segmented into color-texture homogeneous regions. For each region, we define a color name as that used in our daily life. In the retrieval process, images containing regions of same color name as that of the query are selected as candidates. These candidate images are further ranked based on their color and texture features. In this way, the system reduces the ‘semantic gap’ between numerical image features and the rich semantics in the user’s mind. Experimental results show that the proposed system provides promising retrieval results with few features used.