A Gradient Descent Approximation for Graph Cuts

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A Gradient Descent Approximation for Graph Cuts
Abstract. Graph cuts have become very popular in many areas of computer vision including segmentation, energy minimization, and 3D reconstruction. Their ability to find optimal results efficiently and the convenience of usage are some of the factors of this popularity. However, there are a few issues with graph cuts, such as inherent sequential nature of popular algorithms and the memory bloat in large scale problems. In this paper, we introduce a novel method for the approximation of the graph cut optimization by posing the problem as a gradient descent formulation. The advantages of our method is the ability to work efficiently on large problems and the possibility of convenient implementation on parallel architectures such as inexpensive Graphics Processing Units (GPUs). We have implemented the proposed method on the Nvidia 8800GTS GPU. The classical segmentation experiments on static images and video data showed the effectiveness of our method.
Alparslan Yildiz, Yusuf Sinan Akgul
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2009
Where DAGM
Authors Alparslan Yildiz, Yusuf Sinan Akgul
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