Parameter Selection for Graph Cut Based Image Segmentation

This article presents the implementation of methods to improve Graph cut segmentation during initialization by initialization contour. It deals with a modified iterative method for improving the segmentation of heterogeneous objects in the input images. The modifications bring less dependence on user-set constants of Graph cut segmentation and thereby simplify the user interaction. It also deals with a modification for establishing the value of t-links edges in the graph for Graph cut segmentation. These modifications are experimentally tested on the input images intended for segmentation.

2012 Fourth International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN)

Image segmentation with low computational burden has been highly regarded as important goal for researchers. Various image segmentation methods are widely discussed and more noble segmentation methods are expected to be developed when there is rapid demand from the emerging machine vision field. One of the popular image segmentation methods is by using normalised cuts algorithm. It is unfavourable for a high resolution image to have its resolution reduced as high detail information is not fully made used when critical objects with weak edges is coarsened undesirably after its resolution reduced. Thus, a graph-based image segmentation method done in multistage manner is proposed here. In this paper, an experimental study based on the method is conducted. This study shows an alternative approach on the segmentation method using k-means clustering and normalised cuts in multistage manner.

2012

The graph partitioning has been widely used as a mean of image segmentation. One way to partition graphs is through a technique known as Normalized Cut, which analyzes the graph’s Laplacian matrix eigenvectors and uses some of them for the cut. This work proposes the use of Normalized Cut in graphs generated by structures based on Quadtree and Component Tree to perform image segmentation. Experiments of image segmentation by Normalized Cut in these models are made and a specific benchmark compares and ranks the results obtained by other graph-conversion techniques proposed in the literature. The results are promising and allow us to conclude that the use of different graph models combined with the Normalized Cut can yield better segmentations according to the characteristics of images. Keywords-Image Segmentation; Normalized Cut; Quadtree; Component Tree

2006

Abstract. Image segmentation using graph cuts have become very popular in the last years. These methods are computationally expensive, even with hard constraints (seed pixels). We present a solution that runs in time proportional to the number of pixels. Our method computes an ordered region growing from a set of seeds inside the object, where the propagation order of each pixel is proportional to the cost of an optimum path in the image graph from the seed set to that pixel.

2008

Graph cut is a popular technique for interactive image segmentation. However, it has certain shortcomings. In particular, graph cut has problems with segmenting thin elongated objects due to the "shrinking bias". To overcome this problem, we propose to impose an additional connectivity prior, which is a very natural assumption about objects. We formulate several versions of the connectivity constraint and show that the corresponding optimization problems are all NP-hard.

Abstract The analysis of digital scenes often requires the segmentation of connected components, named objects, in images and videos. The problem consists of defining the whereabouts of a desired object (recognition) and its spatial extension in the image (delineation). Humans can outperform computers in recognition, but the other way around is valid for delineation.

2012

ABSTRACT We present a general graph-cut segmentation framework GGC, in which the delineated objects returned by the algorithms optimize the energy functions associated with the lp norm, 1≤ p≤∞. Two classes of well known algorithms belong to GGC: the standard graph cut GC (such as the min-cut/max-flow algorithm) and the relative fuzzy connectedness algorithms RFC (including iterative RFC, IRFC).

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2008

A novel thresholding algorithm is presented in this paper to improve image segmentation performance at a low computational cost. The proposed algorithm uses a normalized graphcut measure as thresholding principle to distinguish an object from the background. The weight matrices used in evaluating the graph cuts are based on the gray levels of the image, rather than the commonly used image pixels. For most images, the number of gray levels is much smaller than the number of pixels. Therefore, the proposed algorithm requires much smaller storage space and lower computational complexity than other image segmentation algorithms based on graph cuts. This fact makes the proposed algorithm attractive in various real-time vision applications such as automatic target recognition. Several examples are presented, assessing the superior performance of the proposed thresholding algorithm compared with the existing ones. Numerical results also show that the normalized-cut measure is a better thresholding principle compared with other graph-cut measures, such as average-cut and average-association ones.

energy function representing the optimal segmentation is formulated. The different terms of the function is determined based on the user input (likelihood model) and a prior model. In this approach, the user marks certain pixels as either " background " or " object ". The likelihood is then calculated using this data. Finally, the energy function is minimized using Graph Cuts based technique. The method was implemented and tested using publicly available benchmark data with ground truth ([11,12]). The results and their analysis are also provided in this report.

International Journal for Research in Applied Science and Engineering Technology

In image processing, segmentation is the process of partitioning digital image into multiple sets of pixels, according to some homogeneity standard. The goal of segmentation is to simplify or change the representation of an image into something that is more meaningful and easier to analyze. Segmentation by computing a minimal cut in a graph is a new and quite general approach for segmenting images. This approach guarantees global solutions, which always find best solution. Graph cut has emerged as a preferred method to solve a class of energy minimization problems such as Image Segmentation. In this paper we used graph cut method to solve image segmentation problem and we got successful results in image segmentation. In this project we proposed a new approach by using optimized normalized cut with combination with K-means algorithm to do the segmentation of static image. In this method we used efficient computational technique based on eigen values and eigen vectors to get optimized segmented image.