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Organizing Web search results into clusters facilitates users' quick browsing through search results. Traditional clustering techniques are inadequate since they don't generate clusters with highly readable names. In this paper, we reformalize the clustering problem as a salient phrase ranking problem. Given a query and the ranked list of documents(More)
Automated cell segmentation and tracking are critical for quantitative analysis of cell cycle behavior using time-lapse fluorescence microscopy. However, the complex, dynamic cell cycle behavior poses new challenges to the existing image segmentation and tracking methods. This paper presents a fully automated tracking method for quantitative cell cycle(More)
Rival penalized competitive learning (RPCL) has been shown to be a useful tool for clustering on a set of sample data in which the number of clusters is unknown. However, the RPCL algorithm was proposed heuristically and is still in lack of a mathematical theory to describe its convergence behavior. In order to solve the convergence problem, we investigate(More)
Gaussian mixture modelling is a powerful tool for data analysis. However, the selection of number of Gaussians in the mixture, i.e., the mixture model or scale selection, remains a difficult problem. In this paper , we propose a new kind of dynamic merge-or-split learning (DMOSL) algorithm on Gaussian mixture such that the number of Gaussians can be(More)
In this letter, we propose an efficient one-nearest-neighbor classifier of texture via the contrast of local energy histograms of all the wavelet subbands between an input texture patch and each sample texture patch in a given training set. In particular, the contrast is realized with a discrepancy measure which is just a sum of symmetrized Kullback-Leibler(More)
The Bayesian Ying–Yang (BYY) harmony learning theory has brought about a new mechanism that model selection on Gaussian mixture can be made automatically during parameter learning via maximization of a harmony function on finite mixture defined through a specific bidirectional architecture (BI-architecture) of the BYY learning system. In this paper, we(More)
As a newly developed 2-D extension of the wavelet transform using multiscale and directional filter banks, the contourlet transform can effectively capture the intrinsic geometric structures and smooth contours of a texture image that are the dominant features for texture classification. In this paper, we propose a novel Bayesian texture classifier based on(More)