This paper addresses the search for a fast and meaningful image segmentation in the context of k-means clustering. The proposed method builds on a widely-used local version of Lloyd’s algorithm, called Simple Linear Iterative Clustering (SLIC). We propose an algorithm which extends SLIC to dynamically adjust the local search, adopting superpixel resolution dynamically to structure existent in t...

Image processing is an important research area in computer vision. Image segmentation plays the vital rule in image processing research. There exist so many methods for image segmentation. Clustering is an unsupervised study. Clustering can also be used for image segmentation. In this paper, an in-depth study is done on different clustering techniques that can be used for image segmentation wit...

In this paper we present some extensions to the k-means algorithm for vector quantization that permit its eecient use in image segmentation and pattern classiication tasks. It is shown that by introducing state variables that correspond to certain statistics of the dynamic behavior of the algorithm, it is possible to nd the representative centers of the lower dimensional manifolds that deene th...

One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to ...

Diseases in fruit cause major problem in agricultural industry and also causes economic loss.The diseases in fruits reduce the yield and also deteriorate the variety and its withdraw from the cultivation.So, earlier detection of symptoms of fruit disease is required.In this paper a solution for identification and classification of fruit diseases is proposed and experimentally validated.Ten diff...

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-...

Image segmentation based on an adaptive K-means clustering algorithm is presented. The proposed method tries to develop K-means algorithm to obtain high performance and efficiency. This method proposes initialization step in K-means algorithm. In addition, it solves a model selection number by determining the number of clusters using datasets from images by frame size and the absolute value bet...

Population ageing is an important global issue. The Taiwanese government has used various Internet of Things (IoT) applications in the “10-year long-term care program 2.0”. It is expected that the efficiency and effectiveness of long-term care services will be improved through IoT support. Home-delivered meal services for the elderly are important for home-based long-term care services. To ensu...

A new method of color image segmentation is proposed in this paper. It’s based on K-means algorithm in HSI space and has the advantage over those based on the RGB space. Both the hue and the intensity components are fully utilized. In the process of hue clustering, the special cyclic property of the hue component is taken into consideration. The paper gives the definition of the distance and th...

In this paper, the idea of applying the k-harmonic means (KHM) technique in biomedical colour image segmentation is presented. The k-means (KM) technique establishes a background for the comparison of clustering techniques. Two original initialization methods for both clustering techniques and two evaluation functions are described. The proposed method of colour image segmentation is completed ...