Spatial kernel K-harmonic means clustering for multi-spectral image segmentation

Spatial Kernel K-Harmonic Means Clustering for Multi-spectral Image Segmentation

The problem of image segmentation using intensity clustering approaches has been addressed in the literature. Grouping pixels of similar intensity to form clusters in an image has been tackled using a number of methods, such as the K-Means (KM) algorithm. The K-Harmonic Means (KHM) was proposed to overcome the sensitivity of KM to centre initialisation. In this paper, we investigate the use of ...

K - Means Clustering for Automatic Image Segmentation

Color Image Segmentation Using a Spatial K-Means Clustering Algorithm

This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In additi...

Kernel Spatial Shadowed C-Means for Image Segmentation

This paper introduces a new image segmentation method in the framework of Shadowed C-Means clustering. By implanting the local spatial information in the estimation procedure of membership values and mapping the original data into a high dimensional Hilbert space, we propose the Kernel Spatial Shadowed C-Means (KSSCM) clustering algorithm. Compared with traditional Fuzzy C-Means and Shadowed C-...

Kernel Fuzzy Similarity Measure-Based Spectral Clustering for Image Segmentation

Spectral clustering has been successfully used in the field of pattern recognition and image processing. The efficiency of spectral clustering, however, depends heavily on the similarity measure adopted. A widely used similarity measure is the Gaussian kernel function where Euclidean distance is used. Unfortunately, the Gaussian kernel function is parameter sensitive and the Euclidean distance ...