Fuzzy mathematical approach to pattern recognition
Fuzzy mathematical approach to pattern recognition
Fuzzy set theoretic measure for automatic feature evaluation
IEEE Transactions on Systems, Man and Cybernetics
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval by Relevance Feedback
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale Fourier descriptors for defect image retrieval
Pattern Recognition Letters
A Relevance Feedback Image Retrieval Scheme Using Multi-Instance and Pseudo Image Concepts
IEICE - Transactions on Information and Systems
Combining intra-image and inter-class semantics for consumer image retrieval
Pattern Recognition
Constructive learning algorithm-based RBF network for relevance feedback in image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
A memory learning framework for effective image retrieval
IEEE Transactions on Image Processing
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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Content-Based Image retrieval has emerged as one of the most active research directions in the past few years. In CBIR, selection of desired images from a collection is made by measuring similarities between the extracted features. It is hard to determine the suitable weighting factors of various features for optimal retrieval when multiple features are used. In this paper, we propose a relevance feedback frame work, which evaluates the features, from fuzzy entropy based feature evaluation index (FEI) for optimal retrieval by considering both the relevant as well as irrelevant set of the retrieved images marked by the users. The results obtained using our algorithm have been compared with the agreed upon standards for visual content descriptors of MPEG-7 core experiments.