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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.

Abstract: This paper presents a non-parametric maximum a posteriori MAP framework for tracking non-rigid video objects. We formulate the region tracking problem as a MAP probability problem and define the probabilistic models in terms of the distances between the intensity distribution of the object and that of its spatial- and temporal-neighborhood. Furthermore, in order to better model the complex intensity changes due to non-rigid movement, we propose to use a non-parametric method to approximate the likelihood and prior terms in the MAP problem. The proposed non-parametric estimation algorithm mostly relies on intensity features and requires no time-consuming motion estimation. Finally, we employ a contour evolution method in the MAP optimization step to iteratively track the object contour. The experimental results demonstrate that the proposed method achieves satisfactory results and outperforms the previous parametric method. [Copyright &y& Elsevier]

Describes a fast computation algorithm for the two-dimensional discrete cosine transform (2-D DCT). Capabilities of the algorithm; Complexity analysis of the post-addition stage.