Semantic clustering and querying on heterogeneous features for visual data
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
NetView: Integrating Large-Scale Distributed Visual Databases
IEEE MultiMedia
Data Resource Selection in Distributed Visual Information Systems
IEEE Transactions on Knowledge and Data Engineering
SemQuery: Semantic Clustering and Querying on Heterogeneous Features for Visual Data
IEEE Transactions on Knowledge and Data Engineering
Color-Based Pseudo Object Model for Image Retrieval with Relevance Feedback
AMCP '98 Proceedings of the First International Conference on Advanced Multimedia Content Processing
eID: a system for exploration of image databases
Information Processing and Management: an International Journal
Relevance Feedback Techniques for Color-based Image Retrieval
MMM '98 Proceedings of the 1998 Conference on MultiMedia Modeling
Efficiently Detecting Arbitrary Shaped Clusters in Image Databases
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
XMage: an image retrieval method based on partial similarity
Information Processing and Management: an International Journal
XMage: An image retrieval method based on partial similarity
Information Processing and Management: an International Journal
Illumination normalization for color face images
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
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Image retrieval based on color content is an auxiliary function for traditional text-annotated image databases. Most color-based image retrieval systems adopt color histograms as the feature of color content. One of the most important steps in these systems is to reduce histogram dimensions with the least loss in color content. A good clustering technique is vital for this purpose. This paper examines the color conservation property by applying different clustering techniques in perceptually uniform color spaces and different images. For studying color spaces, the perceptual uniform spaces, such as Mathematical Transformation to Munsell system (MTM) and C.I.E. L*a*b*, are investigated. For evaluating clustering techniques, the equalized quantization approach, the hierarchical clustering approach, and the Color-Naming-System (CNS) supervised clustering approach are studied. For analyzing color loss, the error bound, the quantized error in color space conversion, and the average quantized error of 400 color images are explored. An image retrieval application based on color content is shown to demonstrate the difference in applying these clustering techniques. The simulation results suggest that good clustering techniques usually lead to more effective retreival.