Iterative refinement by relevance feedback in content-based digital image retrieval
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Evaluating a visualisation of image similarity (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A novel relevance feedback technique in image retrieval
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
A relevance feedback mechanism for content-based image retrieval
Information Processing and Management: an International Journal
Content-based query of image databases: inspirations from text retrieval
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Emergent Semantics through Interaction in Image Databases
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The Truth about Corel - Evaluation in Image Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Texture Features and Learning Similarity
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Interactive Learning with a "Society of Models"
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Approximate Retrieval from Multimedia Databases Using Relevance Feedback
SPIRE '99 Proceedings of the String Processing and Information Retrieval Symposium & International Workshop on Groupware
Relevance Feedback and Category Search in Image Databases
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Query Reformulation for Content Based Multimedia Retrieval in MARS
ICMCS '99 Proceedings of the 1999 IEEE International Conference on Multimedia Computing and Systems - Volume 02
IEEE Transactions on Image Processing
An efficient color representation for image retrieval
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
IEEE Transactions on Circuits and Systems for Video Technology
Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Confidence interval approach to feature re-weighting
Multimedia Tools and Applications
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We introduce a novel relevance feedback method for content-based image retrieval and demonstrate its effectiveness using a subset of the Corel Gallery photograph collection and five low-level colour descriptors. Relevance information is translated into updated, analytically computed descriptor weights and a new query representation, and thus the system combines movement in both query and weight space. To assess the effectiveness of relevance feedback, we first determine the weight set that is optimal on average for a range of possible queries. The resulting multiple-descriptor retrieval model yields significant performance gains over all the single-descriptor models and provides the benchmark against which we measure the additional improvement through relevance feed-back. We model a number of scenarios of user-system interaction that differ with respect to the precise type and the extent of relevance feedback. In all scenarios, relevance feedback leads to a significant improvement of retrieval performance suggesting that feedback-induced performance gain is a robust phenomenon. Based on a comparison of the different scenarios, we identify optimal interaction models that yield high performance gains at a low operational cost for the user. To support the proposed relevant feedback technique we developed a novel presentation paradigm that allows relevance to be treated as a continuous variable.