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Improved graph cut segmentation by learning a contrast model on the fly

McGuinness, Kevin ORCID: 0000-0003-1336-6477 and O'Connor, Noel E. ORCID: 0000-0002-4033-9135 (2013) Improved graph cut segmentation by learning a contrast model on the fly. In: IEEE International Conference on Image Processing (ICIP), 15-18 Sept 2013, Melbourne, Australia.

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Abstract

This paper describes an extension to the graph cut interactive image segmentation algorithm based on a novel approach to addressing the well known small cut problem. The approach uses a generative contrast model to weight interaction potentials. The model attempts to capture the expected changes in color between adjacent pixels in the unlabeled area of the image using the adjacent pixels in the user interactions as training data. We compare our approach to the standard graph cuts algorithm and show that the contrast model allows a user to achieve a more accurate segmentation with fewer interactions. We additionally introduce a variant of the approach based on superpixels that further enhances performance but reduces computational complexity to ensure instant feedback for optimal user experience.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Object segmentation; Interactive segmentation; Graph cuts
Subjects:Computer Science > Interactive computer systems
Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Published in: Proceedings of the IEEE International Conference on Image Processing. . IEEE.
Publisher:IEEE
Official URL:http://www.ieeeicip.org
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:EU Project FP7 AXES ICT-269980, SFI Grant 07/CE/I1147 (CLARITY: Center for Sensor Web Technologies)
ID Code:18398
Deposited On:17 Sep 2013 14:52 by Dr. Kevin McGuinness . Last Modified 24 Jan 2019 15:34

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