The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Recognition using regions
Abstract
This paper presents a unified framework for object detection, segmentation, and classification using regions. Region features are appealing in this context because: (1) they encode shape and scale information of objects naturally; (2) they are only mildly affected by background clutter. Regions have not been popular as features due to their sensitivity to segmentation errors. In this paper, we start by producing a robust bag of overlaid regions for each image using Arbeldez et al., CVPR 2009. Each region is represented by a rich set of image cues (shape, color and texture). We then learn region weights using a max-margin framework. In detection and segmentation, we apply a generalized Hough voting scheme to generate hypotheses of object locations, scales and support, followed by a verification classifier and a constrained segmenter on each hypothesis. The proposed approach significantly outperforms the state of the art on the ETHZ shape database(87.1% average detection rate compared to Ferrari et al. 's 67.2%), and achieves competitive performance on the Caltech 101 database.
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Citations
Cites
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Self-citation
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Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Self-citationRegion-based convolutional networks for accurate object detection and segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2437384 · Cited by 2,912
Self-citationEdge Boxes: Locating Object Proposals from Edges
Zitnick, C. L., & Dollár, P. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_26 · Cited by 2,656
Multiscale Combinatorial Grouping
Arbelaez, P., Pont-Tuset, J., Barron, J., Marques, F., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.49 · Cited by 1,155
Self-citationScalable 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
“Some of these approaches build on bottom-up classless segmentation [9].”
BackgroundSegmentation 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
Metadata
- Authors
- Gu, C., Lim, J. J., Arbelaez, P., & Malik, J.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 1030-1037
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2103897297
- Cited by
- 347
- References
- 10
- FWCI
- 31.62
- Type
- conference paper
- Language
- en
- Open access
- closed
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