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
Retrieving images in fuzzy object-relational databases using dominant color descriptors
Fuzzy Sets 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
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Hi-index | 0.00 |
In this paper, an interactive image retrieval scheme using MPEG-7 visual descriptors is proposed. The performance of image retrieval systems is still limited due to semantic gap, which is created from the discrepancies between the computed low-level features (color, texture, shape, etc.) and user's conception of an image. As a result, more interest has been created towards development of efficient learning mechanism other than designing sophisticated low-level feature extraction algorithms. A simple relevance feedback mechanism is proposed, that learns user's interest and updates feature weights based on a fuzzy feature evaluation measure. This has an advantage of handling comparatively small number of samples over those using standard classifiers involving large number of training samples and having more complexity. Extensive experiments have been performed to test to what extent the performance of an image retrieval system can be enhanced further using MPEG-7 standard visual features at minimum cost.