Interactive image segmentation is a fundamental task in many applications in graphics, image processing, and computational photography. Many leading methods formulate elaborated energy functionals, achieving high performance with reflecting human’s intention. However, they show limitations in practical usage since user interaction is labor intensive to obtain segments efficiently. We present an interactive segmentation method to handle this problem. Our approach, called point cut, requires minimal point supervision only. To this end, we use off-the-shelf object proposal methods that generate object candidates with high recall. With the single point supervision, foreground appearance can be estimated with high accuracy, and then integrated into a graph cut optimization to generate binary segments. Intensive experiments show that our approach outperforms existing methods for interactive object segmentation both qualitatively and quantitatively.
CITATION STYLE
Oh, C., Ham, B., & Sohn, K. (2017). Point-cut: Interactive image segmentation using point supervision. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10111 LNCS, pp. 229–244). Springer Verlag. https://doi.org/10.1007/978-3-319-54181-5_15
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