Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Robust mixture modelling using the t distribution
Statistics and Computing
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
PicHunter: Bayesian Relevance Feedback for Image Retrieval
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Image retrieval with embedded sub-class information using Gaussian mixture models
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Statistical models of video structure for content analysis and characterization
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Generalized nonlinear relevance feedback for interactive content-based retrieval and organization
IEEE Transactions on Circuits and Systems for Video Technology
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The retrieval performance of content-based image retrieval (CBIR) systems is often disappointingly low, mainly due to the subjectivity of human perception. Relevance feedback (RF) has been widely considered as a powerful tool to enhance CBIR systems by incorporating human perception subjectivity into the retrieval procedure. However, usually, the obtained feedback logs are scarce and contain lots of outliers, undermining the RF adaptation effectiveness. In this paper, we tackle these shortcomings exploiting the inherent outlier downweighting capabilities mixtures of Student's t distributions offer. Each semantic class is modeled by a mixture of t distributions fitted to data provided by the system operators. Further, the semantic class models get personalized by application of a novel, efficient RF algorithm allowing for the robust adaptation of the semantic class models to the accumulated feedback of each user. The efficacy of our approach is validated through a series of experiments using objective performance criteria.