ACM Computing Surveys (CSUR)
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Bayesian hierarchical clustering
ICML '05 Proceedings of the 22nd international conference on Machine learning
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Evolutionary spectral clustering by incorporating temporal smoothness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Dirichlet Process Based Evolutionary Clustering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Bayesian order-adaptive clustering for video segmentation
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Smooth image segmentation by nonparametric bayesian inference
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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We present an online video segmentation algorithm based on a novel nonparametric Bayesian clustering method called Bayesian Split-Merge Clustering (BSMC). BSMC can efficiently cluster dynamically changing data through split and merge processes at each time step, where the decision for splitting and merging is made by approximate posterior distributions over partitions with Dirichlet Process (DP) priors. Moreover, BSMC sidesteps the difficult problem of finding the proper number of clusters by virtue of the flexibility of nonparametric Bayesian models. We naturally apply BSMC to online video segmentation, which is composed of three steps--pixel clustering, histogram-based merging and temporal matching. We demonstrate the performance of our algorithm on complex real video sequences compared to other existing methods.