On the choice of similarity measures for image retrieval by example
Proceedings of the tenth ACM international conference on Multimedia
Using Keyblock Statistics to Model Image Retrieval
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Retrieval of difficult image classes using svd-based relevance feedback
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Image annotation: which approach for realistic databases?
Proceedings of the 6th ACM international conference on Image and video retrieval
Rotation Invariant Curvelet Features for Region Based Image Retrieval
International Journal of Computer Vision
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Content-based image retrieval primarily used color distributions as descriptors of the image content; researches have since focused on the use various color representation spaces, color and illumination ivariance, color quentization and color matching. In order to overcome the many limitations of the description by a first-order distribution, several higher-order distributions have been introduced since (like autocorrelogram or color coherence vectors). Although they can perform better, their computational complexity is prihibitive and they require paramenter setting. We Propose to upgrade the first order color distribution (color histogram) by embedding for each color additional information about its perceptual or statistical relevance. Such information is obtained bu using local activity measures such as the Laplacian, the entropy and others. We prove that the new color distribution family is compact, robust and easy to compute and provides a superior retrieval performance, independent with respect to the color representation.