Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Data-Driven Bandwidth Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Improved Fast Gauss Transform and Efficient Kernel Density Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Mean Shift Based Clustering in High Dimensions: A Texture Classification Example
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Simplifying Mixture Models Using the Unscented Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and incorporating top-down cues in image segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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The kernel density estimate (KDE) is a nonparametric density estimate which has broad application in computer vision and pattern recognition. In particular, the mean shift procedure uses the KDE structure to cluster or segment data, including images and video. The usefulness of these twin techniques—KDE and mean shift—on large data sets is hampered by the large space or description complexity of the KDE, which in turn leads to a large time complexity of the mean shift procedure that is superlinear in the number of points. In this paper, we propose a sampling technique for KDE paring, i.e., the construction of a compactly represented KDE with much smaller description complexity. We prove that this technique has good properties in that the pared-down KDE so constructed is close to the original KDE in a precise mathematical sense. We then show how to use this pared-down KDE to devise a considerably faster mean shift algorithm, whose time complexity we analyze formally. Experiments show that image and video segmentation results of the proposed fast mean shift method are similar to those based on the standard mean shift procedure, with the typical speed-up several orders of magnitude for large data sets. Finally, we present an application of the fast mean shift method to the efficient construction of multiscale graph structures for images, which can be used as a preprocessing step for more sophisticated segmentation algorithms.