Example-Driven Semi-automatic Image Collection Segmentation

Binary segmentation for image collection has received considerable attention in image processing and graphic communities recently.Interactively separating foregrounds from an image set one by one is time-consuming and requires tedious user guidance.Meanwhile,image co-segmentation techniques generally lack efficiency due to the complexity of solving large optimization problems and are only applicable to images sharing similar foreground appearance.In this paper,we propose an example-driven semi-automatic framework to tackle the image collection segmentation problem.First,we select few sample images from the given image collection and deliver them to user for hand-segmentation.Then,super-pixel features based support vector machine(SVM) classifier is trained.For each super-pixel of a given image,we estimate its foreground labeling confidence by applying Sigmoid function on the distance between its descriptor and SVM separation hyperplane.The confidence values are then encoded in a graph cut segmentation procedure to achieve automatic object cutout.For each image with incorrectly segmented regions,accurate result is further obtained by a new proposed local refinement process.Experiments on 2 standard datasets are presented,showing that the proposed algorithm not only greatly outperforms existing co-segmentation techniques,but also largely reduces users' efforts for cutting out object interactively.