Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
Readings in uncertain reasoning
Intelligent scissors for image composition
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Isoperimetric Graph Partitioning for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Graph Cuts for Efficient Inference in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2009 papers
Interactive image segmentation by maximal similarity based region merging
Pattern Recognition
Region merging techniques using information theory statistical measures
IEEE Transactions on Image Processing
Fast interactive image segmentation by discriminative clustering
Proceedings of the 2010 ACM multimedia workshop on Mobile cloud media computing
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Efficient and accurate interactive image segmentation have significant importance in many multimedia applications. For mobile touchscreen-based applications, efficiency is more crucial. Moreover, due to small screens of the mobile devices, error tolerance is also a crucial factor. In this paper, a method for interactive image segmentation, tailored for mobile touch screen devices, is proposed. As an interaction methodology, coloring is presented. An automatic stroke-error correction methodology to correct the inaccurate user interaction is also proposed. For the efficient computation of the solution, a novel dynamic and iterative graph-cut solution is formulated. Efficiency and error tolerance of the proposed method are tested by using various sample images. Subjective evaluation of the interactive segmentation algorithms for mobile-touch screen is also performed. Indeed, for the challenging examples, the superior performance of the proposed method is obtained by the experiments.