Image segmentation is an important image processing technique used to analyze the images. Image segmentation is used to separate an image into several “meaningful” parts. Segmentation of image is an old research topic to segmenting the image by its pixel and edge. The main reason to segmenting the image is contain large image variety and the best performance. In this project we develop a novel based approach to segment the image in a better way. We use the RGB color model to get a better segmented image. The Goal of this project is a theoretical and experimental comparison of two popular image segmentation algorithms. The first method is N-Cut method and second is Dual Wavelet Segmentation. On The theoretical side our emphasis will be on describing a common framework in which both of these methods can be expressed. The comparative study is done by using N-cut method and Dual Wavelet Segmentation. The Adaptive Filter and Mean Filter methods are used to filtering the images. N-cut methods lead to over segmentation and it is time consuming for segmenting the images. The Dual Wavelet segmentation give quick result and proper segmentation is done. This confirmed by Graphical representation.
Keywords : Index Term- Image processing, graph cut, Normalized cut, Dual Wavelet, Adaptive Filter