What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilevel Banded Graph Cuts Method for Fast Image Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ACM SIGGRAPH 2006 Papers
A New and Effective Image Retrieval Method Based on Combined Features
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
A comparative evaluation of interactive segmentation algorithms
Pattern Recognition
Interactive image segmentation using probabilistic hypergraphs
Pattern Recognition
Geodesic image and video editing
ACM Transactions on Graphics (TOG)
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In recent years researchers have developed many graph theory based algorithms for image setmentation. However, previous approaches usually require trimaps as input, or consume intolerably long time to get the final results, and most of them just consider the color information. In this paper we proposed a fast object extraction method. First it combines deformable models information with explicit edge information in a graph cuts optimization framework. we segment the input image roughly into two regions: foreground and background. After that, we estimate the opacity values for the pixels nearby the foreground/ background border using belief propagation (BP). Third, we introduce the texture information by building TCP images' co-occurrence matrices. Experiments show that our method is efficient especially for TCP images.