Introduction: Computer Vision Research at NECI
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
A web-based evaluation system for CBIR
MULTIMEDIA '01 Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval
Evaluating image browsers using structured annotation
Journal of the American Society for Information Science and Technology
Content-based visual information retrieval
Distributed multimedia databases
Filter Image Browsing: Interactive Image Retrieval by Using Database Overviews
Multimedia Tools and Applications
MUSE: A Content-Based Image Search and Retrieval System Using Relevance Feedback
Multimedia Tools and Applications
Filter Image Browsing - Exploiting Interaction in Image Retrieval
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
An Architecture of a Web-Based Collaborative Image Search Engine
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
A Framework for Benchmarking in CBIR
Multimedia Tools and Applications
Learning from User Behavior in Image Retrieval: Application of Market Basket Analysis
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
User experiments with the Eurovision cross-language image retrieval system
Journal of the American Society for Information Science and Technology
Visual style: Qualitative and context-dependent categorization
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Image annotation for adaptive enhancement of uncalibrated color images
VISUAL'05 Proceedings of the 8th international conference on Visual Information and Information Systems
Image browsing and navigation using hierarchical classification
IM'99 Proceedings of the 1999 international conference on Challenge of Image Retrieval
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This paper addresses how the effectiveness of a content-based, multimedia information retrieval system can be measured, and how such a system should best use response feedback in performing searches. We propose a simple, quantifiable measure of an image retrieval system's effectiveness, "target testing'', in which effectiveness is measured as the average number of images that a user must examine in searching for a given random target. We describe an initial version of PicHunter, a retrieval system designed to test a novel approach to relevance-feedback. This approach is based on a Bayesian framework that incorporates an explicit model of the user's selection process. PicHunter is intentionally designed to have a minimal, queryless user interface, so that its performance reflects only the performance of the relevance feedback algorithm. The algorithm, however, can easily be incorporated into more traditional, query-based systems. Employing no explicit query, and only a small amount of image processing, PicHunters able to locate randomly selected targets in a database of 4522 images after displaying an average of only 55 groups of 4 images. This is more than 10 times better than random chance. It may be that, with better image processing and some other improvements discussed in this paper, PicHunter can be improved to the point where it is practical on its %own, with only its present, queryless user-interface.