C4.5: programs for machine learning
C4.5: programs for machine learning
ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Probabilistic feature relevance learining for content-based image retrieval
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Information Retrieval
Efficient User-Adaptable Similarity Search in Large Multimedia Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Local versus Global Features for Content-Based Image Retrieval
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Testing for Human Perceptual Categories in a Physician-in-the-loop CBIR System for Medical Imagery
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Relevance Feedback Decision Trees in Content-Based Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Exploring the Nature and Variants of Relevance Feedback
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
An Optimized Interaction Strategy for Bayesian Relevance Feedback
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Image Digestion and Relevance Feedback in the ImageRover WWW Search Engine
Image Digestion and Relevance Feedback in the ImageRover WWW Search Engine
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Visual structures for image browsing
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
A genetic programming framework for content-based image retrieval
Pattern Recognition
Natural Scene Retrieval Based on Graph Semantic Similarity for Adaptive Scene Classification
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Learning to rank for content-based image retrieval
Proceedings of the international conference on Multimedia information retrieval
Weighting visual features with pseudo relevance feedback for CBIR
Proceedings of the ACM International Conference on Image and Video Retrieval
Computer Vision and Image Understanding
| Hi-index | 0.00 |
Database search engines are generally used in a one-shot fashion in which a user provides query information to the system and, in return, the system provides a number of database instances to the user. A relevance feedback system allows the user to indicate to the system which of these instances are desirable, or relevant, and which are not. Based on this feedback, the system modifies its retrieval mechanism in an attempt to return a more desirable instance set to the user. In this paper, we present a relevance feedback technique that uses decision trees to learn a common thread among instances marked relevant. We apply our technique in a preexisting content-based image retrieval (CBIR) system that is used to access high resolution computed tomographic images of the human lung. We compare our approach to a commonly used relevance feedback technique for CBIR, which modifies the weights of a K nearest neighbor retriever. The results show that our approach achieves better retrieval as measured in off-line experiments and as judged by a radiologist who is a lung specialist.