Normalized Cuts and Image Segmentation
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
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
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
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Color Image Segmentation Using Multilevel Clustering Approach
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Semantics Sensitive Segmentation and Annotation of Natural Images
SITIS '08 Proceedings of the 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems
Fast Block Clustering Based Optimized Adaptive Mediod Shift
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Adaptive data driven bandwidth for medoidshift algorithm has been proposed in this work. The proposed method has made it possible to perform clustering on a variety of high resolution statistically different images. Experiments are performed on natural images as well as daily life images. The images have been chosen such that a comparison analysis between fixed sample point estimator k and adaptive k can be carried out in detail. The results show that a fixed value of k=10 is good for statistically compact images but gives undesirable results in dispersed images. Data driven bandwidth is proposed for each data point/ pixel as well as the complete data set/ image. Experimental results have shown our algorithm to be robust. Performance is evaluated on the basis of root mean square error for the quality of clusters.