Searching with known error probability
Theoretical Computer Science
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Hidden Annotation in Content Based Image Retrieval
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
PicHunter: Bayesian Relevance Feedback for Image Retrieval
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
On growing better decision trees from data
On growing better decision trees from data
Introduction: Computer Vision Research at NECI
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Comparing discriminating transformations and SVM for learning during multimedia retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Content-based visual information retrieval
Distributed multimedia databases
Advanced algorithmic approaches to medical image segmentation
MUSE: A Content-Based Image Search and Retrieval System Using Relevance Feedback
Multimedia Tools and Applications
Interactive content-based image retrieval using relevance feedback
Computer Vision and Image Understanding
Dissimilarity computation through low rank corrections
Pattern Recognition Letters
Bayesian Learning for Image Retrieval Using Multiple Features
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Retrieval Performance Improvement through Low Rank Corrections
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Retrieval of difficult image classes using svd-based relevance feedback
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
A novel log-based relevance feedback technique in content-based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Dynamic learning from multiple examples for semantic object segmentation and search
Computer Vision and Image Understanding
Hybrid visual and conceptual image representation within active relevance feedback context
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
MQSearch: image search by multi-class query
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Content-based image retrieval with the normalized information distance
Computer Vision and Image Understanding
Multimedia Tools and Applications
Similarity beyond distance measurement
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Using relevance feedback to bridge the semantic gap
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
| Hi-index | 0.01 |
A new algorithm and systematic evaluation is presented for searching a database via relevance feedback. It represents a new image display strategy for the PicHunter system [2, 1]. The algorithm takes feedback in the form of relative judgments ("item A is more relevant than item B") as opposed to the stronger assumption of categorical relevance judgments ("item A is relevant but item B is not"). It also exploits a learned probabilistic model of human behavior to make better use of the feedback it obtains. The algorithm can be viewed as an extension of indexing schemes like the k-d tree to a stochastic setting, hence the name "stochastic-comparison search." In simulations, the amount of feedback required for the new algorithm scales like log2 |D|, where |D| is the size of the database, while a simple query-by-example approach scales like |D|a, where a