Cosegmentation from similar backgrounds
Fanman Meng, Hongliang Li, King Ngi Ngan, Bing Zeng, Nini Rao · 2014
Recently, the common objects are often required to be extracted from a group of images in many applications, such as video coding and model training. Co-segmentation is a new and efficient method for this requirement. In realistic applications, we observe that the images usually contain similar backgrounds (namely similar scene co-segmentation), such as the city landmark images collected from the web or the key frames sampled from a video. Meanwhile, the existing co-segmentation has not paid so much attention on the similar scene co-segmentation, and the insufficiently accurate segments may be provided by the existing methods. In this paper, we propose an active contours based co-segmentation model to provide foregrounds from the similar backgrounds. We combine the background consistency constraint with the foreground consistency constraint to form the energy function, and use the method of level-set and the calculus of variations to minimize the model. We also speed up the model by the hierarchical structure and the superpixel technique. We test the method on both the image and video dataset. The results show that the proposed model can obtain larger IOU values than the state-of-the-art co-segmentation methods.