The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Contour-based learning for object detection
Abstract
We present a novel categorical object detection scheme that uses only local contour-based features. A two-stage, partially supervised learning architecture is proposed: a rudimentary detector is learned from a very small set of segmented images and applied to a larger training set of un-segmented images; the second stage bootstraps these detections to learn an improved classifier while explicitly training against clutter. The detectors are learned with a boosting algorithm which creates a location-sensitive classifier using a discriminative set of features from a randomly chosen dictionary of contour fragments. We present results that are very competitive with other state-of-the-art object detection schemes and show robustness to object articulations, clutter, and occlusion. Our major contributions are the application of boosted local contour-based features for object detection in a partially supervised learning framework, and an efficient new boosting procedure for simultaneously selecting features and estimating per-feature parameters.
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Cites
Rapid object detection using a boosted cascade of simple features
Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
GrabCut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015720 · Cited by 5,703
Self-citationPictorial Structures for Object Recognition
Felzenszwalb, P. F., & Huttenlocher, D. P. (2005) · International Journal of Computer Vision · doi:10.1023/b:visi.0000042934.15159.49 · Cited by 2,180
Object class recognition by unsupervised scale-invariant learning
Fergus, R., Perona, P., & Zisserman, A. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211479 · Cited by 2,023
"GrabCut": interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM SIGGRAPH 2004 Papers · doi:10.1145/1186562.1015720 · Cited by 935
Self-citationCombined Object Categorization and Segmentation With An Implicit Shape Model
Leibe, B., Leonardis, A., & Schiele, B. (2004) · Cited by 810
Human Detection Based on a Probabilistic Assembly of Robust Part Detectors
Mikolajczyk, K., Schmid, C., & Zisserman, A. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24670-1_6 · Cited by 608
Unsupervised Learning of Models for Recognition
Weber, M., Welling, M., & Perona, P. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45054-8_2 · Cited by 594
Sharing features: efficient boosting procedures for multiclass object detection
Torralba, A., Murphy, K. P., & Freeman, W. T. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315241 · Cited by 577
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Extreme Learning Machine for Multilayer Perceptron
Tang, J., Deng, C., & Huang, G.-B. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2424995 · Cited by 1,451
A survey on face detection in the wild: Past, present and future
Zafeiriou, S., Zhang, C., & Zhang, Z. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.03.015 · Cited by 458
Human detection from images and videos: A survey
Nguyen, D. T., Li, W., & Ogunbona, P. O. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2015.08.027 · Cited by 267
Crisp Boundary Detection Using Pointwise Mutual Information
Isola, P., Zoran, D., Krishnan, D., & Adelson, E. H. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_52 · Cited by 199
Metadata
- Authors
- Shotton, J., Blake, A., & Cipolla, R.
- Venue
- Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
- Publisher
- IEEE
- Published
- 2005
- Pages
- 503-510 Vol. 1
- OpenAlex
- W2123602281
- Cited by
- 287
- References
- 23
- FWCI
- 17.71
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Shotton, J., Blake, A., & Cipolla, R. (2005). Contour-based learning for object detection. Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. https://doi.org/10.1109/iccv.2005.63
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