Using Label Propagation to Get Confidence Map for Segmentation

We propose a novel algorithm to segment objects from the existed segmentation results of the co-segmentation algorithms [1]. Previous co-segmentation algorithms work well when the main regions of the images contain only the target objects; however, their performances degenerate significantly when multi-category objects appear in the images. In contrast, our method adopts mask transformation from multiple images and discriminatively enhancement from multiple object categories, which can effectively ensure a good performance in both scenarios. We propose to use sift-flow [2] between pre-segmented source images and target image, and transform the source images' segmentation mask to fit the target testing image by the flow vectors. Then we use all the transformed masks to vote the testing image mask and get the initial segmentation results. We also propose to use the ratio between the target category and the other categories to eliminate the side effects from other objects that might appeared in the initial segmentation. We conduct our experiment on internet images collected by Rubinstein .etc [1]. We also do additional experiment to study the multi-object conjunction cases. Our algorithm is effective in computation complexity and able to achieve a better performance than the state-of-the-art algorithm.