Swarm 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
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Mean-Shift Tracking via a New Similarity Measure
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Probabilistic tracking in joint feature-spatial spaces
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
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Mean shift, like other gradient ascent optimization methods, is susceptible to local maxima, and hence often fails to find the desired global maximum In this paper, mean shift segmentation method based on hybridized particle swarm optimization algorithm is proposed which overcomes the shortcoming of mean shift The mean shift vector is firstly optimized using hybridized PSO algorithm when performing the new algorithm Then, the optimal mean shift vector is updated using mean shift procedure Experimental results show that the proposed algorithm used for image segmentation can segment images more effectively and provide more robust segmentation results.