Audio-visual human recognition using semi-supervised spectral learning and hidden Markov models
Journal of Visual Languages and Computing
A Subword Normalized Cut Approach to Automatic Story Segmentation of Chinese Broadcast News
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
Interactive shadow removal from a single image using hierarchical graph cut
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Multicue graph mincut for image segmentation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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Self-validation and efficient global optimization are two important objectives for feature space clustering. The Gibbs energy minimization of Markov random field (MRF) provides a general framework for the clustering problem. However, the large computational burdenmakes mostMRFbased methods cannot efficiently achieve these two targets simultaneously. In this paper, we propose a fast clustering approach which is self-validated and guarantees stepwise global optimum. We use the net-structured MRF (NS-MRF) to model the feature space and present an iterative cluster evolution algorithm. For each iteration, the cluster evolving is chosen from three hypotheses, i.e., cluster remaining, cluster merging or cluster splitting, in terms of energy minimization. Graph cuts are used to obtain the optimal binary splitting while taking spatial coherence into account. We terminate the evolution process when the whole energy of NS-MRF stops decreasing, thus solve the validation problem. We also provide experimental results and compare our approach with the state of arts.