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Image segmentation have been a topic of research in computer vision domain for decades. There have been a multitude of methods proposed for segmentation, but most have been dependent on a high level user input which guides the contour or boundaries towards the real boundaries. In order to come close to a fully automated or partially automated solution, a novel method is proposed for performing multilabel, interactive image segmentation using Random Walk algorithm as the fundamental driver of segmentation. The problem is formulated as follows: given a small number of pixels with userdefined (or predefined) labels, assign the the probability that a random walker starting at each unlabeled pixel will first reach one of the prelabeled pixels. The current pixel is then assigned the label corresponding to the max of this probability. This leads to highquality segmentations of an image into $K$ different components. The algorithm is based on image graphs, where image pixels are represented as graphs connected by edges to its 8connected neighbours. In this paper, a novel approach to $K$class image segmentation problem is proposed which utilizes userdefined seeds representing the example regions of the image belonging to $K$ objects. Each seed specifies a location with a userdefined label. The algorithm labels an unseeded pixel by resolving the question: Given a random walker starting at this location, what is the probability that it first reaches each of the K seed points? It will be shown that this calculation may be performed exactly without the simulation of a random walk. By performing this calculation, the algorithm assigns a Ktuple vector to each pixel that specifies the probability that a random walker starting from each unseeded pixel will first reach each of the K seed points. A final segmentation may be derived from these Ktuples by selecting for each pixel the most probable seed destination for a random walker. The graph weights are determined to be a function of the pixel intensities, specifically $w_{ij}$ = $exp((g_i  g_j)^2)$. The algorithm works by biasing the random walker to avoid crossing sharp intensity gradients, which leads to a quality segmentation that respects object boundaries (including weak boundaries). The algorithm exposes only one free variable $\beta$, and can be combined with other approaches involving pre and postfiltering techniques. Additionally, the algorithm provides onthefly correction of previous detected boundary in an computationally efficient way.
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