Automatic people segmentation with a template-driven graph cut

International Journal of Computer …, 2008

This paper focuses on segmenting human from photo images. It has found several applications like album making, photo classification and image retrieval. The result can be further applied to many useful applications like part recognition which can be further applied to gesture analysis as well as in tracking. Segmenting human from photo images is still a challenge because of numerous real world factors like shading, image noise, occlusions, and background clutter and also because of great variability of shapes, poses, clothes etc. Previous works on human segmentation requires shape-matching processes. In this paper, we propose a simple method to automatically recover human bodies from photos. We use some haar cascades to detect human body that is haar cascade_upperbody and haar cascade_lowerbody which helps in performing upperbody and lower body segmentation. We need to perform CT (coarse torso) detection using MCTD algorithm for accurate upper body segmentation. Lower body is then extracted accurately using MOH based graph-cut algorithm. Experimental results show that, the proposed algorithm works well on VOC 2006 and VOC 2010 data set for segmenting person with various poses. Thus achieving high performance compared to conventional methods.

Lecture Notes in Computer Science, 2013

This paper presents a new approach to segment and track multiple persons in a video sequence via graph-cuts optimization technique. In fact, first, we extract the initial silhouettes that will be modeled by ellipses. Then, a prediction step based on optical flow vectors allows us to detect if an occlusion will handle in the next frame. Hence, we identify the occluding persons by the use of the chi-squared similarity metric based on the intensity histogram and we update the objects models of the interacting persons. Finally, a segmentation based on graph-cuts optimization is performed based on the predicted models. The experimental results show the efficiency of our algorithm to track multiple persons even under occlusion.

Computer Vision–ECCV 2006, 2006

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.

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.

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.

This paper presents a new algorithm for human segmentation in images. The human silhouette is estimated in positive windows that are already obtained with an existing efficient detection method. This accurate segmentation uses the data previously computed in the detection. First, a pre-segmentation step computes the likelihood of contour segments as being a part of a human silhouette. Then, a contour segment oriented graph is constructed from the shape continuity cue and the prior cue obtained by the pre-segmentation. Segmentation is so posed as the computation of the shortest-path cycle which corresponds to the human silhouette. Additionally, the process is achieved iteratively to eliminate irrelevant paths and to increase the segmentation performance. The approach is tested on a human image database and the segmentation performance is evaluated quantitatively.

Scientific Reports, 2012

We present a scale-invariant, template-based segmentation paradigm that sets up a graph and performs a graph cut to separate an object from the background. Typically graph-based schemes distribute the nodes of the graph uniformly and equidistantly on the image, and use a regularizer to bias the cut towards a particular shape. The strategy of uniform and equidistant nodes does not allow the cut to prefer more complex structures, especially when areas of the object are indistinguishable from the background. We propose a solution by introducing the concept of a ''template shape'' of the target object in which the nodes are sampled non-uniformly and non-equidistantly on the image. We evaluate it on 2D-images where the object's textures and backgrounds are similar, and large areas of the object have the same gray level appearance as the background. We also evaluate it in 3D on 60 brain tumor datasets for neurosurgical planning purposes.

Computer Vision Theory and Applications, 2010

Human detection and segmentation is a challenging task owing to variations in human pose and clothing. The union of Histograms of Oriented Gradients based descriptors and of a Support Vector Machine classifier is a classic and efficient method for human detection in the images. Conversely, as often in detection, accurate segmentation of these persons is not performed. Many applications however need it. This paper tackles the problem of giving rise to information that will guide the final segmentation step. It presents a method which uses the union mention above to relate to each contour segment a likelihood degree of being part of a human silhouette. Thus, data previously computed in detection are used in the pre-segmentation. A human silhouette database was ceated for learning. Figure 1: Input image (a), contour image computed with Canny algorithm (b), contour pixels gathered in contour segments (c) and likely segments (d).