Threshold-optimized decision-level fusion and its application to biometrics
Pattern Recognition
Exploring the boundary region of tolerance rough sets for feature selection
Pattern Recognition
A feature extraction method for use with bimodal biometrics
Pattern Recognition
Multimodal image retrieval via Bayesian information fusion
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Bagging Constraint Score for feature selection with pairwise constraints
Pattern Recognition
Entropy regularization, automatic model selection, and unsupervised image segmentation
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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This paper presents a novel framework called fuzzy relevance feedback in interactive content-based image retrieval systems. Conventional binary labeling in relevance feedback requires crisp decisions to be made on the relevance of the retrieved images. This is restrictive as user interpretation of image similarity is imprecise and nonstationary in nature and may vary with respect to different information needs and perceptual subjectivity. It is, therefore, inadequate to model the user perception of image similarity with crisp binary logic. In view of this, we propose a soft relevance notion to integrate the users' fuzzy perception of visual contents into the framework of relevance feedback. A progressive fuzzy radial basis function network is proposed to learn the user information need by optimizing a cost function. An efficient gradient descent-based learning strategy is then employed to estimate the underlying network parameters. Experimental results based on a database of 10 000 images demonstrate the effectiveness of the proposed method.