The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Object cosegmentation
Abstract
Cosegmentation is typically defined as the task of jointly segmenting “something similar” in a given set of images. Existing methods are too generic and so far have not demonstrated competitive results for any specific task. In this paper we overcome this limitation by adding two new aspects to cosegmentation: (1) the “something” has to be an object, and (2) the “similarity” measure is learned. In this way, we are able to achieve excellent results on the recently introduced iCoseg dataset, which contains small sets of images of either the same object instance or similar objects of the same class. The challenge of this dataset lies in the extreme changes in viewpoint, lighting, and object deformations within each set. We are able to considerably outperform several competitors. To achieve this performance, we borrow recent ideas from object recognition: the use of powerful features extracted from a pool of candidate object-like segmentations. We believe that our work will be beneficial to several application areas, such as image retrieval.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
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Hosang, J., Benenson, R., Dollar, P., & Schiele, B. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2465908 · Cited by 727
Detection of Co-salient Objects by Looking Deep and Wide
Zhang, D., Han, J., Li, C., Wang, J., & Li, X. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0907-4 · Cited by 328
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
Wang, W., Lu, X., Shen, J., Crandall, D., & Shao, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00933 · Cited by 303
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01834 · Cited by 182
Robust Object Co-Segmentation Using Background Prior
Han, J., Quan, R., Zhang, D., & Nie, F. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2781424 · Cited by 169
Metadata
- Authors
- Vicente, S., Rother, C., & Kolmogorov, V.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 2217-2224
- OpenAlex
- W2295160225
- Cited by
- 292
- References
- 2
- FWCI
- 21.28
- Type
- conference paper
- Language
- en
- Open access
- closed
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