In this paper, we propose a fast image segmentation method based on normalized cut. This method apply simple linear iterative clustering super-pixel algorithm to obtain super-pixel regions, and then use affinity propagation clustering to extract the representative pixels in each super-pixel regions, Finally, we apply normalized cut to obtain segmentation results. At the end of the paper, Numeri...

We describe an annealing procedure that computes the normalized N-cut of a weighted graph G. The first phase transition computes the solution of the approximate normalized 2-cut problem, while the low temperature solution computes the normalized N-cut. The intermediate solutions provide a sequence of refinements of the 2-cut that can be used to split the data to K clusters with 2 </= K </= N. T...

Learning models and related results depend on the quality of the input data. If raw data is not properly cleaned and structured, the results are tending to be incorrect. Therefore, discretization as one of the preprocessing techniques plays an important role in learning processes. The most important challenge in the discretization process is to reduce the number of features’ values. This operat...

We discuss approaches to transduction based on graph cut cost functions. More specifically, we focus on the normalized cut, which is the cost function of choice in many clustering applications, notably in image segmentation. Since optimizing the normalized cut cost is an NP-complete problem, much of the research attention so far has gone to relaxing the problem of normalized cut clustering to t...

The image segmentation problem is to delineate, or segment, a salient feature in an image. As such, this is a bipartition problem with the goal of separating the foreground from the background. An NP-hard optimization problem, the Normalized Cut problem, is often used as a model for image segmentation. The common approach for solving the normalized cut problem is the spectral method which gener...

Grouping by graph partitioning is an effective engine for perceptual organization. This graph partitioning process, mainly motivated by computational efficiency considerations, is usually implemented as recursive bi-partitioning, where at each step the graph is broken into two parts based on a partitioning measure. We study four such measures, namely, the minimum cut [11], average cut [6], Shi-...