Determining number of clusters and prototype locations via multi-scale clustering
Pattern Recognition Letters
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
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
On optimum choice of k in nearest neighbor classification
Computational Statistics & Data Analysis
KNN-kernel density-based clustering for high-dimensional multivariate data
Computational Statistics & Data Analysis
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In clustering analysis of remote sensing imagery, a commonly held assumption is that the feature space can be modelled as a mixture of Gaussians. However, the assumption is not true for many real data and therefore incorrect classification results are often obtained by parametric methods. Nonparametric methods in feature space analysis can avoid the use of the normality assumption. Arbitrarily structured feature spaces can be analysed only by means of nonparametric methods as these methods do not have embedded assumptions. The mean shift is a basic computational module of the nonparametric technique in pattern recognition. The mean shift procedure can be used to cluster multispectral remote sensing imagery. Earlier clustering techniques based on the mean shift used a single scale over the entire feature space and were not feasible for the analysis of complex multimodal feature spaces. In this paper, we present an adaptive mean shift method in which local scale information is involved. The proposed algorithm can find arbitrary density, size and shape clusters in remote sensing imagery. The method is a simple technique of unsupervised image classification. We demonstrate its advantages in classification accuracy over earlier methods described in this paper.