Learning image semantics from users relevance feedback
Proceedings of the 12th annual ACM international conference on Multimedia
Content-based sub-image retrieval using relevance feedback
Proceedings of the 2nd ACM international workshop on Multimedia databases
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Sketch retrieval and relevance feedback with biased SVM classification
Pattern Recognition Letters
Ego-similarity measurement for relevance feedback
Expert Systems with Applications: An International Journal
Hidden annotation for image retrieval with long-term relevance feedback learning
Pattern Recognition
Learning in region-based image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Online learning of relevance feedback from expert readers for mammogram retrieval
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Image retrieval using transaction-based and SVM-based learning in relevance feedback sessions
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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In this paper we address several aspects of the learning problem in content-based image retrieval (CBIR).First, we introduce the linear and kernel-based biased discriminant analysis, or BiasMap,to fit the unique nature of relevance feedback as a small sample biased classification problem.Secondly, a WARF (word associationvia relevance feedback) formula is presented for learning keyword relations during the process of relevance feedback.We also introduce our new ser interface for CBIR, ImageGrouper, which is designed to support more sophisticated user feedbacks and annotations.Finally,we se the D-EM (Discriminant-EM)algorithm as a way of exploiting unlabeled data in CBIR and offer some insights as to when unlabeled data will help.