Image Segmentation Via Iterative Geodesic Averaging

We present a simple and fast method for performing unsupervised segmentation. Our method works by centering a square window on each pixel of the input image. Each pixel is then assigned to a new color which is computed by averaging the pixel colors inside the window. The idea is that if this averaging operation is repeated a few times then we should obtain an image in which pixels of the same color surface are assigned to the same (or at least to very similar) color values. Consequently, the desired color segments are formed by groups of spatially neighboring pixels that share the same color in the convolved image. Obviously, our method would deliver poor performance if the averaging operation is applied in a naive manner, as pixel colors of different segments would be mixed. To overcome this problem, we propose to compute a geodesic weight mask that regulates a pixel´s influence in the averaging operation. A pixel´s weight in the window is determined by computing the geodesic distance to the center pixel. In other words, we enforce that a pixel obtains high influence only if there exists a path to the center pixel along which the color does not change significantly (connectivity). The proposed method is evaluated on some widely used test images. Our method seems to produce accurate segmentation results and to capture object outlines correctly. We show by quantitative evaluation that our segmentation algorithm outperforms two competing segmentation methods.