Image to Image Search using K-means Clustering

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...

Leukemia Image Segmentation using K-Means Clustering and HSI Color Image Segmentation

During the unfolding measures that are taken for the purpose of leukemia detection, segmentation of blood cells is a vital step. In this paper two approaches of such segmentation technique is proposed. While one uses K-means clustering, other uses color image based segmentation method. Both the processes segment the image into two regions, blasts & backgrounds. These blasts are our area of inte...

K-Means Clustering using Tabu Search with Quantized Means

The Tabu Search (TS) metaheuristic has been proposed for K-Means clustering as an alternative to Lloyd’s algorithm, which for all its ease of implementation and fast runtime, has the major drawback of being trapped at local optima. While the TS approach can yield superior performance, it involves a high computational complexity. Moreover, the difficulty in parameter selection in the existing TS...

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...