ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A relevance feedback approach for content based image retrieval using gaussian mixture models
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Region-based semantic similarity propagation for image retrieval
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Multimedia Tools and Applications
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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This paper presents a generalized Bayesian framework for relevance feedback in content-based image retrieval. The proposed feedback technique is based on the Bayesian learning method and incorporates a time-varying user model into the formulation. We define the user model with two terms: a target query and a user conception. The target query is aimed to learn the common features from relevant images so as to specify the user's ideal query. The user conception is aimed to learn a parameter set to determine the time-varying matching criterion. Therefore, at each feedback step, the learning process updates not only the target distribution, but also the target query and the matching criterion. In addition, another objective of this paper is to conduct the relevance feedback on images represented in region level. We formulate the matching criterion using a weighting scheme and proposed a region clustering technique to determine the region correspondence between relevant images. With the proposed region clustering technique, we derive a representation in region level to characterize the target query. Experiments demonstrate that the proposed method combined with time-varying user model indeed achieves satisfactory results and our proposed region-based techniques further improve the retrieval accuracy.