The Image Segmentation Based On K-Means With MAP

Image Segmentation Using Semi-Supervised k-Means

Extracting the region of interest is a very challenging task in Image Processing. Image segmentation is an important technique for image processing which aims at partitioning the image into different homogeneous regions or clusters. Lots of general-purpose techniques and algorithms have been developed and widely applied in various application areas. In this paper, a Semi-Supervised k-means segm...

K - Means Clustering for Automatic Image Segmentation

K-Means Cluster Analysis for Image Segmentation

Does K-Means reasonably divides the data into k groups is an important question that arises when one works on Image Segmentation? Which color space one should choose and how to ascertain that the k we determine is valid? The purpose of this study was to explore the answers to aforementioned questions. We perform K-Means on a number of 2-cluster, 3cluster and k-cluster color images (k>3) in RGB ...

Color Image Segmentation Using K-means Classification on Rgb Histogram

-The paper presents the approach of Color Image Segmentation Using k-means Classification on RGB Histogram. The kmeans is an iterative and an unsupervised method. The existing algorithms are accurate, but missing the locality information and required high-speed computerized machines to run the segmentation algorithms. The proposed method is content-aware and feature extraction method, which is ...

Unsupervised Image Segmentation Method based on Finite Generalized Gaussian Distribution with EM & K-Means Algorithm

In Image Processing Model Based Image Segmentation plays a dominant role in Image Analysis and Image Retrieval . Recently much work has been reported regarding Image Segmentation based on Finite Gaussian Mixture Models using EM algorithm. (Yiming Wu et al (2003)) , (Yamazaki.T (1998)). However, in some images the pixel intensities inside the image regions may not be MesoKurtic or Bell Shaped, b...