MUSE: A Content-Based Image Search and Retrieval System Using Relevance Feedback
Multimedia Tools and Applications
Image Retrieval by Regions: Coarse Segmentation and Fine Color Description
VISUAL '02 Proceedings of the 5th International Conference on Recent Advances in Visual Information Systems
Color Image Retrieval Based on Primitives of Color Moments
VISUAL '02 Proceedings of the 5th International Conference on Recent Advances in Visual Information Systems
Constraint Based Region Matching for Image Retrieval
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Improving retrieval performance by region constraints and relevance feedback
Journal of Computer Science and Technology
Mining user hidden semantics from image content for image retrieval
Journal of Visual Communication and Image Representation
About the Embedding of Color Uncertainty in CBIR Systems
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
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In this work, an efficient low-dimensional color indexing scheme for region-based image retrieval is presented. The colors in each image region are first quantized so that only a small number of cluster centroids are needed to represent the region color information. The proposed color feature descriptor consists of these quantized colors and their percentages in the region. A similarity distance measure is defined and shown to be equivalent to the quadratic color histogram distance measure. The quantized colors are indexed in the 3-D color space so that high-dimensional indexing can be avoided. During the search process, each quantized color in the query is used as a separate cue to find matches containing that color. The matches from all the query colors are then joined to obtain the final retrievals. Experimental results show that the proposed scheme is fast and accurate compared to the color histogram approach.