The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Constrained parametric min-cuts for automatic object segmentation
Abstract
We present a novel framework for generating and ranking plausible objects hypotheses in an image using bottom-up processes and mid-level cues. The object hypotheses are represented as figure-ground segmentations, and are extracted automatically, without prior knowledge about properties of individual object classes, by solving a sequence of constrained parametric min-cut problems (CPMC) on a regular image grid. We then learn to rank the object hypotheses by training a continuous model to predict how plausible the segments are, given their mid-level region properties. We show that this algorithm significantly outperforms the state of the art for low-level segmentation in the VOC09 segmentation dataset. It achieves the same average best segmentation covering as the best performing technique to date, 0.61 when using just the top 7 ranked segments, instead of the full hierarchy in. Our method achieves 0.78 average best covering using 154 segments. In a companion paper, we also show that the algorithm achieves state-of-the art results when used in a segmentation-based recognition pipeline.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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
Graph Cuts and Efficient N-D Image Segmentation
Boykov, Y., & Funka-Lea, G. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7934-5 · Cited by 1,910
Cited by
Selective Search for Object Recognition
Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980
Training Region-Based Object Detectors with Online Hard Example Mining
Shrivastava, A., Gupta, A., & Girshick, R. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.89 · Cited by 2,686
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680
Scalable Object Detection Using Deep Neural Networks
Erhan, D., Szegedy, C., Toshev, A., & Anguelov, D. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.276 · Cited by 1,161
“In order to address this issue a variety of methods were proposed, varying from detector cascades, to using segmentation to suggest a small number of object hypotheses [14, 2, 4].”
BackgroundMethodSimultaneous Detection and Segmentation
Hariharan, B., Arbeláez, P., Girshick, R., & Malik, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_20 · Cited by 1,085
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
Kong, T., Yao, A., Chen, Y., & Sun, F. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.98 · Cited by 934
Segmentation as selective search for object recognition
van de Sande, K. E. A., Uijlings, J. R. R., Gevers, T., & Smeulders, A. W. M. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126456 · Cited by 739
What Makes for Effective Detection Proposals?
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
How good are detection proposals, really?
Hosang, J., Benenson, R., & Schiele, B. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.24 · Cited by 215
Contextual Priming and Feedback for Faster R-CNN
Shrivastava, A., & Gupta, A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_20 · Cited by 153
Monocular SLAM Supported Object Recognition
Pillai, S., & Leonard, J. (2015) · Robotics: Science and Systems XI · doi:10.15607/rss.2015.xi.034 · Cited by 118
“Variants of these include Constrained-Parametric Min-cuts (CPMC) [6], Selective Search [33], Edge Boxes [36], Binarized Normed Gradients (BING) [9].”
MethodRegionlets for Generic Object Detection
Wang, X., Yang, M., Zhu, S., & Lin, Y. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389830 · Cited by 107
Object Localization Based on Proposal Fusion
Tang, S., Li, Y., Deng, L., & Zhang, Y. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2729786 · Cited by 25
Metadata
- Authors
- Carreira, J., & Sminchisescu, C.
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 3241-3248
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2017691720
- Cited by
- 420
- References
- 3
- FWCI
- 31.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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