Optimization of relevance feedback weights
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modern Information Retrieval
Content-Based Image Retrieval Systems
ASSET '99 Proceedings of the 1999 IEEE Symposium on Application - Specific Systems and Software Engineering and Technology
Bayesian Relevance Feedback for Content-Based Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
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
Using Bayesian classifier in relevant feedback of image retrieval
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Set estimation via ellipsoidal approximations
ICASSP '95 Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02
Journal of Cognitive Neuroscience
Automatic image semantic annotation based on image-keyword document model
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
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This paper proposes a new Bayesian method for content-based image retrieval using relevance feedback. In this method, the problem of content-based image retrieval is first formulated as a two-class classification problem, where each image in the database can be classified as "relevant" or "nonrelevant" with respect to the query and the goal is to minimize the misclassification error. Then, the problem of image retrieval is further transferred into a simpler problem of ranking each image in the database by using a similarity measure that is basically a likelihood ratio. Here, the likelihood of the relevant class is modeled by a mixture of Gaussian distribution determined by the positive samples, and the likelihood of the non-relevant class is assumed to be an average of Gaussian kernels centered at negative samples. The experimental results have indicated that the proposed method has potential to become practical for content-based image retrieval.