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Unsupervised Joint Object Discovery and Segmentation in Internet Images
Open accessAbstract
We present a new unsupervised algorithm to discover and segment out common objects from large and diverse image collections. In contrast to previous co-segmentation methods, our algorithm performs well even in the presence of significant amounts of noise images (images not containing a common object), as typical for datasets collected from Internet search. The key insight to our algorithm is that common object patterns should be salient within each image, while being sparse with respect to smooth transformations across other images. We propose to use dense correspondences between images to capture the sparsity and visual variability of the common object over the entire database, which enables us to ignore noise objects that may be salient within their own images but do not commonly occur in others. We performed extensive numerical evaluation on established co-segmentation datasets, as well as several new datasets generated using Internet search. Our approach is able to effectively segment out the common object for diverse object categories, while naturally identifying images where the common object is not present.
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Kang, K., Li, H., Yan, J., Zeng, X., Yang, B., Xiao, T., Zhang, C., Wang, Z., Wang, R., Wang, X., et al. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2736553 · Cited by 554
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Karsch, K., Liu, C., & Kang, S. B. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2316835 · Cited by 453
Self-citationJetley, S., Lord, N. A., Lee, N., & Torr, P. H. S. (2018) · arXiv · doi:10.48550/arxiv.1804.02391 · Cited by 383
Detection of Co-salient Objects by Looking Deep and Wide
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Han, J., Quan, R., Zhang, D., & Nie, F. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2781424 · Cited by 169
Object-Proposal Evaluation Protocol is ‘Gameable’
Chavali, N., Agrawal, H., Mahendru, A., & Batra, D. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.97 · Cited by 65
Neural Congealing: Aligning Images to a Joint Semantic Atlas
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Metadata
- Authors
- Rubinstein, M., Joulin, A., Kopf, J., & Liu, C.
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 1939-1946
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1996140089
- Cited by
- 355
- References
- 0
- FWCI
- 24.32
- Type
- conference paper
- Language
- en
- Open access
- green
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