Image segmentation based on graph cut(pixel based) technique

Abstract

Image segmentation is an important image processing technique which is used to analyze what is inside the image. Image segmentation is used to separate an image into several “meaningful” parts. It is an old research topic, which started around 1970, but there is still no robust solution toward it. There are two main reasons, the first is that the content variety of images is too large, and the second one is that there is no benchmark standard to judge the performance. In this project we develop a novel based approach to segment the image in a more better way.in this project we use the Ohta color model instead of RGB color model to get a better segmented image.

Graph cut algorithms are successfully applied to a wide range of problems in vision and graphics. Here we used this graph cut technique to solve the image segmentation problem. And we got successful results in partitioning an in image. In this project we use the normalized cut technique to do the segmentation of an image .in this method we use an efficient computational technique based on the eigenvalues and eigenvectors to get an optimized segmented image. We have applied this approach to segment the static images.