Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature normalization and likelihood-based similarity measures for image retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Upgrading Color Distributions for Image Retrieval: Can We Do Better?
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
A Bayesian Framework for Semantic Content Characterization
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Probabilistic visual learning for object detection
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Unifying View of Image Similarity
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Negative pseudo-relevance feedback in content-based video retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
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In image retrieval systems, a variety of simple similarity measures are used. The choice for one similarity measure or another is generally driven by an experimental comparison on a labeled database. The drawback of such an approach is that, while a large number of possible similarity measures can be tested, we do not know how to extend from the obtained results. However, the choice of a good similarity measure leads to noticeable better results. It is known that this choice is related to the variability of the images within the same class. Therefore, we propose a model of image retrieval systems and deduce a scheme for deriving the best similarity measure in a set of similarity measures, assuming a parametric model of the variability of feature vectors within the same class. An experimental validation of the model and the derived similarity measures is performed on synthetic ground-truth databases. Finally, from our experiments, we give several rules to follow for the design of ground-truth databases allowing reliable conclusions on the search of better similarity measures.