Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Retrieval Performance Improvement through Low Rank Corrections
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Empirical Evaluation of Dissimilarity Measures for Color and Texture
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
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This paper demonstrates an approach to image retrieval by classifying images into different semantic categories and using probabilistic similarity measures. To reduce the semantic-gap based on low-level features, a relevance feedback mechanism is also added, which refines the query parameters to adjust the matching functions. First and second order statistical parameters (mean and covariance matrix) are pre-computed from the feature distributions of predefined categories on multivariate Gaussian assumption. Statistical similarity measure functions utilize these category specific parameters based on the online prediction of a multi-class support vector machine classifier. In relevance feedback, user selected positive or relevant images are used for calculating new query point and updating statistical parameters in each iteration. Whereas, most prominent relevant and non-relevant category specific information are utilized to modify the ranking of the final retrieved images. Experimental results on a generic image database with ground-truth or known categories are reported. Performances of several probabilistic distance measures are evaluated, which show the effectiveness of the proposed technique.