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Weighted pseudo-metric discriminatory power improvement using a bayesian logistic regression model based on a variational method.
| Content Provider | CiteSeerX |
|---|---|
| Author | Ziou, D. Dubeau, F. Colin, B. Ksantini, R. |
| Abstract | Distance measures like the nearest neighbor rule distance and 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, thereby 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 based on a variational method, in order to compute the weights of a pseudo-metric, to improve its discriminatory capacity and then to increase image retrieval accuracy. This pseudo-metric makes use of the compressed and quantized versions of the Daubechies-8 wavelet decomposed feature vectors, and its weights were adjusted by the classical logistic regression. The evaluation and comparison of the Bayesian logistic regression model and the classical logistic regression one are performed independently of and in image retrieval context. Experimental results show that the Bayesian logistic regression model is a significantly better tool than the classical logistic regression model to compute the pseudo-metric |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Feature Vector Cbir Problem Discriminatory Capacity Euclidean Distance Classical Logistic Regression Model Quantized Version Retrieval Performance Image Retrieval Context Pseudo-metric Discriminatory Power Improvement Bayesian Logistic Regression Model Image Retrieval Accuracy Variational Method Daubechies-8 Wavelet Probabilistic Approach Content-based Image Retrieval Distance Measure Effective Solution Local Relevance Experimental Result Classical Logistic Regression Neighbor Rule Distance Irrelevant Feature Probability Distribution |
| Content Type | Text |
