Let us look again at the requirements for the segmentation algorithm. Methods that more or less meet the requirements are called oversegmentation or super pixel methods. They're called so because we can segment a region of one object for a larger number of fragments. Oversegmentation is the process by which the objects being segmented from the background are themselves segmented or fractured into subcomponents. During years of research into the problem of image segmentation, many methods have been developed, starting with heuristic methods like Region growing, which we specify a set of initial points and gradually attached to these points neighboring pixels that have the same characteristics. Other approaches includes split and merge, where we divide the image into regions until the resultant regions are homogeneous, and energy-based methods. One formula is the segmentation task in the form of optimization of contours and then iteratively updates these contours. Probably, the most advanced yet relatively simple approaches are clustering-based ones. Let us discuss a few of those in more detail. One of the efficient oversegmentation schemes is the Mean shift algorithm. In Mean shift, one represents every pixel in the image with a feature vector, color, histogram of gradients and pixels of filtered image may serve as features. The idea of this approach is to search the feature space of both pixels for areas with large probability density mass, most of high dimensional feature distribution. In this slide, we display the points in the feature space and find the density maximum. To perform the search for a local density maximum, we examined the neighborhood of every point in the feature space and estimate the local density via the non-parametric estimate known from statistics such as Kernel or Parzen estimates. We may then compute the shift, the direction in the image where density increases in the feature space. When the procedure is complete for all points in the sample, we cluster points according to the simplest possible rule. A cluster is a group of points for which the search procedure leads to the same mode of distribution. You can see on the slide that Mean shift divides the segments fairly well by the colors. Having this in mind, one may note that image segmentation is nothing more than clustering in the feature space represented in pixels. Lots of well-known clustering algorithms are based on k-means clustering, an approach dividing points using some kind of a distance in the feature space. SLIC or simple linear iterative clustering is another unsupervised segmentation method which is base on k-means and adopted to image segmentation task. The idea of this algorithm is to compare the pixels that are spaced apart from each other in the image by a distance not more than s. The hyperparameter s can later adjust the size of super pixels. K-means requires initial approximation of cluster centers. In SLIC, uniformly distributed points separated by the distance s are used. As in the k-means algorithm, the cluster satirists are updated until the summary change in all clusters is less than the operating threshold. Here you can see that SLIC method outperforms other oversegmentation methods not only by the quality but in fact also by the computation coast. In summary, oversegmentation as an approach to segment images iteratively by splitting them into smaller subsegments, a lot of approaches are developed and most of them are based on k-means clustering. That concludes this section.