The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning a classification model for segmentation
Abstract
We propose a two-class classification model for grouping. Human segmented natural images are used as positive examples. Negative examples of grouping are constructed by randomly matching human segmentations and images. In a preprocessing stage an image is over-segmented into super-pixels. We define a variety of features derived from the classical Gestalt cues, including contour, texture, brightness and good continuation. Information-theoretic analysis is applied to evaluate the power of these grouping cues. We train a linear classifier to combine these features. To demonstrate the power of the classification model, a simple algorithm is used to randomly search for good segmentations. Results are shown on a wide range of images.
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Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Cited by
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00371 · Cited by 11,953
“Segmentation is a broad field: there’s interactive segmentation [57, 109], edge detection [3], super pixelization [85], object proposal generation [2], foreground segmentation [94], semantic segmentation [90], instance segmentation [66], panoptic segmentation [59], etc.”
BackgroundBiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation
Yu, C., Gao, C., Wang, J., Yu, G., Shen, C., & Sang, N. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01515-2 · Cited by 1,813
Semantic Image Segmentation via Deep Parsing Network
Liu, Z., Li, X., Luo, P., Loy, C.-C., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.162 · Cited by 675
Hoiem, D., Efros, A. A., & Hebert, M. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073232 · Cited by 616
Lazy Random Walks for Superpixel Segmentation
Shen, J., Du, Y., Wang, W., & Li, X. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2302892 · Cited by 339
Explaining the Explainer: A First Theoretical Analysis of LIME
Garreau, D., & von Luxburg, U. (2020) · arXiv · doi:10.48550/arxiv.2001.03447 · Cited by 143
Semantic structure from motion with points, regions, and objects
Bao, S. Y., Bagra, M., Chao, Y.-W., & Savarese, S. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247992 · Cited by 107
On the Binding Problem in Artificial Neural Networks
Greff, K., van Steenkiste, S., & Schmidhuber, J. (2020) · arXiv · doi:10.48550/arxiv.2012.05208 · Cited by 102
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
Local spatial constraint and total variation for hyperspectral anomaly detection
Feng, R., Li, H., Wang, L., Zhong, Y., Zhang, L., & Zeng, T. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3098814 · Cited by 46
Müller, H., & Holzinger, A. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103546 · Cited by 30
Metadata
- Authors
- Ren, & Malik
- Venue
- Proceedings Ninth IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2003
- Pages
- 10-17 vol.1
- OpenAlex
- W2104125540
- Cited by
- 1,765
- References
- 1
- FWCI
- 10.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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