Local spectral method to seeded image cosegmentation

Abstract

The cosegmentation problem is referred to as segmenting the same or similar objects simultaneously from a group of images. However, designing a robust and efficient cosegmentation algorithm is a challenging work because of the variety and complexity of the object and the background. We proposed a new seeded image cosegmentation method based on a local spectral method, which combines bottom-up information and seeds’ knowledge effectively for segmentation. Multiple images are connected into a weighted undirected graph so the cosegmentation problem is converted into a graph partitioning problem that is solved by biased normalized cuts. The results of the cosegmentation experiment reveal that the proposed method performs well even in the presence of some noise images (images not containing a common object) or in the condition of the image set containing more than one object.