Hierarchical Unsupervised Object Segmentation with Manifold Regularization
Pages 266 - 270
Abstract
In this paper, we address the problem of object segmentation in an unsupervised way that performs image segmentation without annotated training images. To this end, we integrate low-level visual similarities with high-level semantic correlations by manifold regularization for detecting meaningful object segments. Low-level visual similarities are measured by a linear distance metric. And high-level semantic correlations are implicitly approximated from visual representations among different objects with the assumption that visual structures within an object are limited. Moreover, a hierarchical graph cut algorithm is developed for multi-class object segmentation. Finally, experimental results show a promising performance of the proposed approach on natural images.
References
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Chen, F., Yu, H., et al. 2013. Shape sparse representation for joint object classification and segmentation. IEEE Trans. Image Processing. 22(3), 992--1004.
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Hu, H., Feng, J., Yu, C., et al. 2013. Multi-class constrained normalized cut with hard, soft, unary and pairwise priors and its applications to object segmentation. IEEE Trans. Image Processing. 22(11), 4328--4340.
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Russell, B., C., Freeman, W., T., et al. 2006. Using multiple segmentations to discover objects and their extent in image collections. In CVPR.
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Liu, G., Lin, Z., et al. 2010. Unsupervised object segmentation with a hybrid graph model (HGM). IEEE Trans. PAMI. 32(5), 910--924.
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Index Terms
- Hierarchical Unsupervised Object Segmentation with Manifold Regularization
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Information & Contributors
Information
Published In
July 2014
430 pages
ISBN:9781450328104
DOI:10.1145/2632856
- General Chairs:
- Hanzi Wang,
- Larry Davis,
- Program Chairs:
- Wenwu Zhu,
- Stephan Kopf,
- Yanyun Qu,
- Publications Chairs:
- Jun Yu,
- Jitao Sang,
- Tao Mei
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In-Cooperation
- NSF of China: National Natural Science Foundation of China
- Beijing ACM SIGMM Chapter
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 July 2014
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- Research
- Refereed limited
Conference
ICIMCS '14
ICIMCS '14: International Conference on Internet Multimedia Computing and Service
July 10 - 12, 2014
Xiamen, China
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Overall Acceptance Rate 163 of 456 submissions, 36%
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