DIP: Final project report Image segmentation based on the normalized cut framework YuNing Liu ChungHan Huang WeiLun Chao R98942125 R98942117 R98942073  Image segmentation is an important image processing, and it seems everywhere if we want to analyze what inside the image. For example, if we seek to find if there is a chair or person inside an indoor image, we may need image segmentation to separate objects and analyze each object individually to check what it is. Image segmentation usually serves as the preprocessing before image pattern recognition, image feature extraction and image compression. Researches of it started around 1970, while there is still no robust solution, so we want to find the reason and see what we can do to improve it. Our final project title is a little bit different from the proposal. The title of the proposal is “Photo Labeling Based on Texture Feature and Image Segmentation”, while during the execution, we change it into ”Image segmentation based on the normalized cut framework”. The main reason is that we found there are many kinds of existed image segmentation techniques and methods, in order to gain enough background, we went through several surveys and decided to change the title into a deep view of image segmentation.   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. For example, in figure 1.1, we show an original image and two segmented images based on different kinds of image segmentation methods. The one of figure 1.1 (b) separates the sky into several parts while the figure 1.1 (c) misses some detail in the original image. Every technique has its own advantages also disadvantages, so it’s hard to tell which one is better. There are tons of previous works about image segmentation, great survey resources could be found in [1, 2, 3]. From these surveys, we could simply separate the image segmentation techniques into three different classes (1)   ,