Identical object segmentation through level sets with similarity constraint

Unsupervised identical object segmentation remains a challenging problem in vision research due to the difficulties in obtaining high-level structural knowledge about the scene. In this paper, we present an algorithm based on level set with a novel similarity constraint term for identical objects segmentation. The key component of the proposed algorithm is to embed the similarity constraint into curve evolution, where the evolving speed is high in regions of similar appearance and becomes low in areas with distinct contents. The algorithm starts with a pair of seed matches (e.g. SIFT) and evolve the small initial circle to form large similar regions under the similarity constraint. The similarity constraint is related to local alignment with assumption that the warp between identical objects is affine transformation. The right warp aligns the identical objects and promotes the similar regions growth. The alignment and expansion alternate until the curve reaches the boundaries of similar objects. Real experiments validates the efficiency and effectiveness of the proposed algorithm.