Unsupervised image segmentation using global spatial constraint and multi-scale representation on multiple segmentation proposals

This paper presents a novel method for unsupervised image segmentation. The method determines the reasonable segments for final segmentation by exploiting both global and local context cues on multiple segmentation proposals. The proposal is obtained by using any existing segmentation algorithms, providing the diverse segment cues to guide segmentation. An iterative process is used to perform the cues integration and the image segmentation, including the segments modeling and the segments labeling. The former estimates the distribution of shared segments, while the latter labels each proposal into segments by minimizing an energy function. The final segmentation is produced when the consistent spatial layout is found in different proposals. Compared with the existing methods, the segmentation results are more satisfying on the Berkeley Segmentation Database.