Feature normalization and likelihood-based similarity measures for image retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Bayesian parameter estimation via variational methods
Statistics and Computing
Learning Feature Relevance and Similarity Metrics in Image Databases
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
A Weighted Distance Approach to Relevance Feedback
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Classification Error Rate for Quantitative Evaluation of Content-based Image Retrieval Systems
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
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Distance measures like the Euclidean distance have been the most widely used to measure similarities between feature vectors in the content-based image retrieval (CBIR) systems. However, in these similarity measures no assumption is made about the probability distributions and the local relevances of the feature vectors. Therefore, irrelevant features might hurt retrieval performance. Probabilistic approaches have proven to be an effective solution to this CBIR problem. In this paper, we use a Bayesian logistic regression model, in order to compute the weights of a pseudo-metric to improve its discriminatory capacity and then to increase image retrieval accuracy. The pseudo-metric weights were adjusted by the classical logistic regression model in [Ksantini et al., 2006]. The Bayesian logistic regression model was shown to be a significantly better tool than the classical logistic regression one to improve the retrieval performance. The retrieval method is fast and is based on feature selection. Experimental results are reported on the Zubud and WANG color image databases proposed by [Deselaers et al., 2004].