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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 6/2016

01-12-2016 | Original Article

A cue integration method for anaglyph image partition

Authors: Qin Wu, Guodong Guo, Jiuzhen Liang

Published in: International Journal of Machine Learning and Cybernetics | Issue 6/2016

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Abstract

Image content analysis is important for automated image organization, labeling, and search. Partitioning an image into meaningful regions is one of the fundamental problems in image analysis. Anaglyph images and videos are more and more popular, such as in Flickr and YouTube. The anaglyph images provide disparity cue in a single image, which could be useful for image analysis. This paper exploits disparity cue for image partition. An image partition method for anaglyph is proposed. The disparity or depth cue is integrated with the traditional single-view image segmentation. A concept called dominant disparity is proposed, corresponding to each single-view image segment, which largely tolerates the disparity errors and image over-segmentations. A cue integration algorithm is developed. The integration is at the level of image segments rather than pixels, and object-level image segmentation is achieved. Experiments on both synthetic and real anaglyph images demonstrate the effectiveness of the proposed image partition method for anaglyph image analysis. To the best of our knowledge, our work is for the first time to perform anaglyph image partition.
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Metadata
Title
A cue integration method for anaglyph image partition
Authors
Qin Wu
Guodong Guo
Jiuzhen Liang
Publication date
01-12-2016
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 6/2016
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0304-7

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