The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Entropy rate superpixel segmentation
Abstract
We propose a new objective function for superpixel segmentation. This objective function consists of two components: entropy rate of a random walk on a graph and a balancing term. The entropy rate favors formation of compact and homogeneous clusters, while the balancing function encourages clusters with similar sizes. We present a novel graph construction for images and show that this construction induces a matroid - a combinatorial structure that generalizes the concept of linear independence in vector spaces. The segmentation is then given by the graph topology that maximizes the objective function under the matroid constraint. By exploiting submodular and mono-tonic properties of the objective function, we develop an efficient greedy algorithm. Furthermore, we prove an approximation bound of ½ for the optimality of the solution. Extensive experiments on the Berkeley segmentation benchmark show that the proposed algorithm outperforms the state of the art in all the standard evaluation metrics.
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Cites
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
An analysis of approximations for maximizing submodular set functions—II
Fisher, M. L., Nemhauser, G. L., & Wolsey, L. A. (1978) · Mathematical Programming Studies · doi:10.1007/bfb0121195 · Cited by 589
Recovering Occlusion Boundaries from a Single Image
Hoiem, D., Stein, A. N., Efros, A. A., & Hebert, M. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408985 · Cited by 206
Cited by
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
Roth, H. R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E. B., & Summers, R. M. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24553-9_68 · Cited by 784
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
Roth, H. R., Lu, L., Lay, N., Harrison, A. P., Farag, A., Sohn, A., & Summers, R. M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.01.006 · Cited by 325
Seo, H., Badiei Khuzani, M., Vasudevan, V., Huang, C., Ren, H., Xiao, R., Jia, X., & Xing, L. (2020) · Medical Physics · doi:10.1002/mp.13649 · Cited by 300
Human Parsing with Contextualized Convolutional Neural Network
Liang, X., Xu, C., Shen, X., Yang, J., Liu, S., Tang, J., Lin, L., & Yan, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.163 · Cited by 245
Metadata
- Authors
- Liu, M.-Y., Tuzel, O., Ramalingam, S., & Chellappa, R.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 2097-2104
- OpenAlex
- W2135431554
- Cited by
- 1,026
- References
- 3
- FWCI
- 18.89
- Type
- conference paper
- Language
- en
- Open access
- closed
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