Graph Cut Algorithms in Vision, Graphics and Machine Learning

Sudipta Sinha

Graph Cut Algorithms in Vision, Graphics and Machine Learning

Sudipta Sinha

MSR-TR-2004-152 | December 2004

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This integrative paper studies graph-cut and network flow algorithms on graphs and compares its applications towards solving diverse problems in Computer Vision, Computer Graphics and Machine Learning. The following three papers form the core of this comparative study.

An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision by Boykov et.al.[1]
Graph Cut Textures: Image and Video Synthesis using Graph Cuts’ by Kwatra et. al. [4]
Semi Supervised Learning using Randomized Mincuts by Blum et.al. [5]

Graph Cut Algorithms in Vision, Graphics and Machine Learning

An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision by Boykov et.al.[1]

Graph Cut Textures: Image and Video Synthesis using Graph Cuts’ by Kwatra et. al. [4]

Semi Supervised Learning using Randomized Mincuts by Blum et.al. [5]