Kernel Methods for Weakly Supervised Mean Shift Clustering
Mean shift clustering is a powerful unsupervised data analysis technique which does not require prior knowledge of the number of clusters, and does not constrain the shape of the clusters. The data association criteria is based on the underlying probability distribution of the data points which is defined in advance via the employed distance metric. In many problem domains, the initially designed distance metric fails to resolve the ambiguities in the clustering process. We present a novel...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of IEEE International Conference on Computer Vision (ICCV 2009)|
|01_Tuzel_Kernel_Methods_for_Weakly_2009.pdf||2.61 MB||Adobe PDF||Request a copy|
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