An efficient method for color image segmentation using adaptive mean shift and normalized cuts

Abstract

In the proposed method, a combined approach of Adaptive Mean Shift and Normalized Cuts is used for clustering the images. In this method, both color and gray scale images can be segmented effectively and it requires less computational complexity. In the first stage, the image is divided into different segments using Adaptive Mean Shift algorithm and the segments generated are labeled and the labeled segments are represented as nodes in a graph. The result obtained by applying the Adaptive Mean Shift algorithm is given to the normalized cut method for grouping the clustered segments. Experimental result shows that the proposed method gives better performance in terms of segments than other methods when tested with color and gray scale natural images.