Implementation of Image Segmentation with Graph Cut Method

Image segmentation is the process of subdividing a digital image into its systematized regions or objects which is useful in image analysis. In this review paper, we carried out an organized survey of many image segmentation techniques which are flexible, cost effective and computationally more efficient. We classify these segmentation methods into three categories: the traditional methods, graph theoretical methods and combination of both traditional and graph theoretical methods. In the second and third category of image segmentation approaches, the image is modeled as a weighted and undirected graph. Normally a pixel or a group of pixels are connected with nodes. The edge weights represent the dissimilarity between the neighborhood pixels. The graph or the image is then divided according to a benchmark designed to model good clusters. Every partition of the nodes or the pixels as output from these algorithms is measured as an object segment in an image representing a graph. Some of the popular algorithms are thresholding, normalized cuts, iterated graph cut, clustering method, watershed transformation, minimum cut, grey graph cut, and minimum spanning treebased segmentation.

Jurnal Teknologi, 2015

Graph cut is an interactive segmentation method. It works based on preparing graph from image and finds the minimum cut for the graph. The edges value is calculated based on belonging a pixel to object or background. The advantage of this method is using the cost function. If the cost function is clearly described, graph cut is presents a generally optimum result. In this paper graph concepts and preparing graph according to image pixels is described. Preparing different edges and performing min cut/max flow is explained. Finally, the method is applied on some medical images.

Combinatorial graph cut algorithms have been successfully applied to a wide range of problems in computer vision and graphics. In this paper we have analyzed mainly four techniques Graph cuts (GC), Iterative Graph Cuts (IGC), Multi-label Random walker (RW) and Lazy Snapping (LS). Graph Cut techniques are used for completely automatic high-level grouping of image pixels. Iterative Graph Cut technique allows some sort of user interaction for extracting objects from a complex background. Random Walker algorithm requires user specific labels and produce a segmentation where each segment is connected to a labeled pixel. Lazy Snapping provides instant visual feedback, snapping the cutout contour to the true object boundary efficiently despite the presence of ambiguous or low contrast edges.

2013 7th Asia Modelling Symposium, 2013

Normalised cut method has been effectively used for image segmentation by representing an image as weighted graph in global view. It does segmentation via partitioning the graphs into sub-graphs. Clustering algorithm is implemented such that sub-graphs with common similarities are grouped together into one cluster and separates sub-graphs that are dissimilar into distinctive clusters. Clustered segments from the normalised cuts are then produced. As the clusters initialisation gives influence to the segmentation result, optimisation of the clustering algorithm is implemented to achieve better segmentation. With the approach applied in the normalised cuts based image segmentation, the constraint of using normalised cuts algorithm in image segmentation can be alleviated. In this paper, evaluation of the clustering algorithm with the normalised cuts image segmentation on images has been carried out and the effect of different image complexity towards normalised cuts segmentation process is presented.

An image can be considered as a group of nodes as vertices and edges as links. Various techniques are formed based upon this assumption and energy minimization. Graph cut is one of the promising techniques for image segmentation. Boykov and Kolmogorov use mincut/ maxflow graph principal for image segmentation. We have discussed some other techniques such as grab cut which is user interactive and very effective technique. In last the current problems were also highlighted.

Segmentation is considered as an important step in image processing and computer vision applications, which divides an input image into various non-overlapping homogenous regions and helps to interpret the image more conveniently. This paper presents an efficient image segmentation algorithm using neutrosophic graph cut (NGC). An image is presented in neutrosophic set, and an indeterminacy filter is constructed using the indeterminacy value of the input image, which is defined by combining the spatial information and intensity information. The indeterminacy filter reduces the indeterminacy of the spatial and intensity information. A graph is defined on the image and the weight for each pixel is represented using the value after indeterminacy filtering. The segmentation results are obtained using a maximum-flow algorithm on the graph. Numerous experiments have been taken to test its performance, and it is compared with a neutrosophic similarity clustering (NSC) segmentation algorithm and a graph-cut-based algorithm. The results indicate that the proposed NGC approach obtains better performances, both quantitatively and qualitatively.

Image segmentation plays a crucial role in effective understanding of digital images. Past few decades saw hundreds of research contributions in this field. However, the research on the existence of general purpose segmentation algorithm that suits for variety of applications is still very much active. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. This paper critically reviews existing important graph based segmentation methods. The review is done based on the classification of various segmentation algorithms within the framework of graph based approaches. The major four categorizations we have employed for the purpose of review are: graph cut based methods, interactive methods, minimum spanning tree based methods and pyramid based methods. This review not only reveals the pros in each method and category but also explores its limitations. In addition, the review highlights the need for creating a database for benchmarking intensity based algorithms, and the need for further research in graph based segmentation for automated real time applications.

We present a novel graph-based approach to image segmentation which can be applied to either greyscale or color images. The assumption is that nearby pixels with similar colors or greyscale intensities may belong to the same region or segment of the image. A graph representation for an image is derived from the similarity between the pixels, and then partitioned by a computationally efficient graph clustering method, which first identifies representative nodes for each cluster and then expands them to obtain complete clusters of the graph. Experiments with synthetic and natural images are presented. A comparison with the well known normalized cut method shows that our approach can be faster and produces segmentations that are in better agreement with visual assessment of the original images.

University Of California–Irvine

This project addresses the problem of segmenting an image into different regions. We analyze two unsupervised learning algorithms namely the K-means and EM and compare it with a graph based algorithm, the Normalized Cut algorithm. The K-means and EM are clustering algorithms,which partition a data set into clusters according to some defined distance measure. The Normalized Cut criterion takes a measure of the similarity between data elements of a group and the dissimilarity between different groups for segmenting the images.

Procedings of the British Machine Vision Conference 2008, 2008

The graph cut based approach has become very popular for interactive segmentation of the object of interest from the background. One of the most important and yet largely unsolved issues in the graph cut segmentation framework is parameter selection. Parameters are usually fixed beforehand by the developer of the algorithm. There is no single setting of parameters, however, that will result in the best possible segmentation for any general image. Usually each image has its own optimal set of parameters. If segmentation of an image is not as desired under the current setting of parameters, the user can always perform more interaction until the desired results are achieved. However, significant interaction may be required if parameter settings are far from optimal. In this paper, we develop an algorithm for automatic parameter selection. We design a measure of segmentation quality based on different features of segmentation that are combined using AdaBoost. Then we run the graph cut segmentation algorithm for different parameter values and choose the segmentation of highest quality according to our learnt measure. We develop a new way to normalize feature weights for the AdaBoost based classifier which is particularly suitable for our framework. Experimental results show a success rate of 95.6% for parameter selection.

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.

2018

This paper presents an image segmentation technique using discreet tools from graph theory. The image segmentation incorporating graph theoretic methods make the formulation of the problem suppler and the computation more ingenious. In our proposed method, the problem is modeled by partitioning a graph into several sub-graphs; in such a way that each of the subgraphs represents an eloquent region of the image. The segmentation is performed in a spatially discrete space by the efficient tools from graph theory. After the brief literature review, we have formulated the problem using graph representation of image and the threshold function. The borders between the different regions in an image are identified as per the segmentation criteria and, later, the partitioned regions are branded with random colors. In our approach, in order to make the segmentation fast, the image is preprocessed by DWT and coherence filter before performing the segmentation. We have carried out the experiment...

Graphics, Patterns …, 2010

Graph partitioning, or graph cut, has been studied by several authors as a way of image segmenting. In the last years, the Normalized Cut has been widely used in order to implement graph partitioning, based on the graph spectra analysis (eigenvalues and eigenvectors). This area is known as Spectral Graph Theory. This work uses a hierarchical structure in order to represent images, the Component Tree. We provide image segmentation based on Normalized Cut, with image representation based on the Component Tree and on its scale-space analysis. Experimental results present a comparison between other image representations, as pixel grids, including multiscale graph decomposition formulation, and Watershed Transform. As the results show, the proposed approach, applied to different images, presents satisfying image segmentation.

Lecture Notes in Computer Science, 2008

We present a novel graph-based approach to image segmentation. The objective is to partition images such that nearby pixels with similar colors or grayscale intensities belong to the same segment. A graph representing an image is derived from the similarity between the pixels and partitioned by a computationally efficient graph clustering method, which identifies representative nodes for each cluster and then expands them to obtain complete clusters of the graph. Experiments with synthetic and natural images are presented. A comparison with the well known graph clustering method of normalized cuts shows that our approach is faster and produces segmentations that are in better agreement with visual assessment on original images.