Color Image Segmentation via Improved K-Means Algorithm

A new color model for digital image segmentation was proposed, the experimental results show the efficiency of using this model comparing with other existing models

Key words: Neural network, image segmentation, RGB color space, color histogram, image analysis, color signature, digital image recognition, poison and non-poison

In section 4 the proposed method for the defect segmentation of fruits based on color using K-means clustering technique is presented and discussed.. Section 5

In this paper, a novel density based k-means clustering algorithm has been proposed to overcome the drawbacks of DBSCAN and kmeans clustering algorithms.. The result will be

Proposed by Dorigo in 1991, the ant colony algorithm is widely used in medical image segmentation because of its excellent clustering characteristics [12].. On the basis of

Image segmentation is a very first step in processing the mammogram image for further analysis. Segmentation of mammograms can be done in two basic steps. In the first step we aim

Motivated with this, we propose, in this paper, the image segmentation and classification done using K-Means and Laplacian of Gaussian filters or Prewitt Filter. Our algorithm has

The obtained results for the K-means method: (a) the image segmented with nine clusters; and (b) the binary image obtained from the segmentation process, where 0 value corresponds