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
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Mean Shift Is a Bound Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Measure for Objective Evaluation of Image Segmentation Algorithms
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
Mean shift: An information theoretic perspective
Pattern Recognition Letters
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We point out a difference between the original mean-shift formulation of Fukunaga and Hostetler and the common variant in the computer vision community, namely whether the pairwise comparison is performed with the original or with the filtered image of the previous iteration. This leads to a new hybrid algorithm, called Color Mean Shift, that roughly speaking, treats color as Fukunaga's algorithm and spatial coordinates as Comaniciu's algorithm. We perform experiments to evaluate how different kernel functions and color spaces affect the final filtering and segmentation results, and the computational speed, using the Berkeley and Weizmann segmentation databases. We conclude that the new method gives better results than existing mean shift ones on four standard comparison measures (∼ 15%, 22% improvement on RAND and BDE measures respectively for color images), with slightly higher running times (∼ 10%). Overall, the new method produces segmentations comparable in quality to the ones obtained with current state of the art segmentation algorithms.