This paper introduces the Automated Two-Dimensional K-Means (A2DKM) algorithm, a novel unsupervised clustering technique. The proposed technique differs from the conventional clustering techniques because it eliminates the need for users to determine the number of clusters. In addition, A2DKM incorporates local and spatial information of the data into the clustering analysis. A2DKM is qualitati...

This paper presents an approach for a real-time region-based motion segmentation and tracking using an adaptive thresholding and k-means clustering in a scene, with focus on a video monitoring system. In order to reduce the computational load to the motion segmentation, the presented approach is based on the variation regions application of a weighted k-means clustering algorithm, followed by a...

Overview of Selected Segmentation Approaches Segmentation approaches can range from throwing darts at the data to human judgment and to advanced cluster modeling. We will explore four such methods: factor segmentation, k-means clustering, TwoStep cluster analysis, and latent class cluster analysis. Factor Segmentation Factor segmentation is based on factor analysis. The first step is to factor-...

It is still a challenging task to segment real-world images, since they are often distorted by unknown noise and intensity inhomogeneity. To address these problems, we propose a novel segmentation algorithm via a local correntropy-based K-means (LCK) clustering. Due to the correntropy criterion, the clustering algorithm can decrease the weights of the samples that are away from their clusters. ...

In this paper we present a comparative study of MR brain image segmentation techniques. The aim of this study is to assess the robustness and accuracy of three most commonly used unsupervised segmentation methods k-means (KM), FCM and EM. KM is a well known hard segmentation method for quicker processing whereas FCM and EM are popularly used soft segmentation methods particularly for brain tiss...

Image segmentation is a basic but important preprocessing to image recognition in computer vision applications. In this paper, we propose a pixel-based k-means (PKM) clustering to generate superpixels, which comprise many pixels with similar colors and neighbor positions. In contrast with conventional center-based clustering, the PKM method traces several nearer clustering centers for a pixel i...

A combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map was used to perform image segmentation and edge detection tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging procedures based on mean intensity value to segment the image regions...

Abstract The performance of image segmentation highly relies on the original inputting image. When the image is contaminated by some noises or blurs, we can not obtain the efficient segmentation result by using direct segmentation methods. In order to efficiently segment the contaminated image, this paper proposes a two step method based on the hybrid total variation model with a box constraint...

In this paper, we present reliable algorithms for fuzzy k-means and C-means that could improve MRI segmentation. Since the k-means or FCM method aims to minimize the sum of squared distances from all points to their cluster centers, this should result in compact clusters. Therefore the distance of the points from their cluster centre is used to determine whether the clusters are compact. For th...

This paper applies a recently-developed neural clustering scheme, called "probabilistic winner-take-all (PWTA)", to image segmentation. Experimental results are presented. These results show that the PWTA clustering scheme signiicantly outperforms the popular k-means algorithm when both are utilized to segment a synthetic-aperture-radar (SAR) image representing ship targets in an open-ocean scene.