Clustering web images using association rules, interestingness measures, and hypergraph partitions
ICWE '06 Proceedings of the 6th international conference on Web engineering
Adaptive image retrieval based on the spatial organization of colors
Computer Vision and Image Understanding
Semantic analysis of real-world images using support vector machine
Expert Systems with Applications: An International Journal
Learning color names for real-world applications
IEEE Transactions on Image Processing
SIEVE: search images effectively through visual elimination
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Region based image retrieval incorporated with camera metadata
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Transportation Distances on the Circle
Journal of Mathematical Imaging and Vision
WISE'06 Proceedings of the 7th international conference on Web Information Systems
Deep into color names: matching color descriptions by their fuzzy semantics
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
Color naming models for color selection, image editing and palette design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Performance of traditional content-based image retrieval systems is far from userýs expectation due to the ýsemantic gapý between low-level visual features and the richness of human semantics. In attempt to reduce the ýsemantic gapý, 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 ýsemantic gapý between numerical image features and the rich semantics in the userýs mind. Experimental results show that the proposed system provides promising retrieval results with few features used.