Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
NeTra: a toolbox for navigating large image databases
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Learning in region-based image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Combining Color and Shape Features for Image Retrieval
UAHCI '09 Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction. Part III: Applications and Services
SIEVE: search images effectively through visual elimination
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
A Pragmatic Approach for Qualitative Shape and Qualitative Colour Similarity Matching
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Wavelet based information for retrieval and classification of mammographic images
Proceedings of the 2011 International Conference on Communication, Computing & Security
Integrating semantic templates with decision tree for image semantic learning
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
A model for the qualitative description of images based on visual and spatial features
Computer Vision and Image Understanding
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Due to the ‘semantic gap' between low-level visual features and the rich semantics in user's mind, performance of traditional content-based image retrieval systems is far from user's expectation. In attempt to reduce the ‘semantic gap', this paper introduces a region-based image retrieval system with high-level semantic color names used. For each segmented region, we define a perceptual color as the low-level color feature of the region. This perceptual color is then converted to a semantic color name. In this way, the system reduces the ‘semantic gap' between numerical image features and the richness of human semantics. Four different ways to calculate perceptual color are studied. Experimental results confirm the substantial performance of the proposed system compared to traditional CBIR systems.