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Guiding model search using segmentation
Abstract
In this paper we show how segmentation as preprocessing paradigm can be used to improve the efficiency and accuracy of model search in an image. We operationalize this idea using an over-segmentation of an image into superpixels. The problem domain we explore is human body pose estimation from still images. The superpixels prove useful in two ways. First, we restrict the joint positions in our human body model to lie at centers of superpixels, which reduces the size of the model search space. In addition, accurate support masks for computing features on half-limbs of the body model are obtained by using agglomerations of superpixels as half limb segments. We present results on a challenging dataset of people in sports news images
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Cited by
Multi-scale Features for Detection and Segmentation of Rocks in Mars Images
Dunlop, H., Thompson, D. R., & Wettergreen, D. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383257 · Cited by 55
Metadata
- Authors
- Mori, G.
- Venue
- Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
- Publisher
- IEEE
- Published
- 2005
- Pages
- 1417-1423 Vol. 2
- OpenAlex
- W2110248298
- Cited by
- 298
- References
- 0
- FWCI
- 5.73
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Mori, G. (2005). Guiding model search using segmentation. Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. https://doi.org/10.1109/iccv.2005.112
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
