Quiet: Faster Belief Propagation for Images and Related Applications
Authors: Yasuhiro Fujiwara, Dennis Shasha
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our approach is significantly faster than existing approaches without sacrificing inference quality. |
| Researcher Affiliation | Collaboration | NTT Software Innovation Center, 3-9-11 Midori-cho Musashino-shi, Tokyo, 180-8585, Japan Department of Computer Science, New York University, 251 Mercer Street, New York, NY 10012, USA fujiwara.yasuhiro@lab.ntt.co.jp, shasha@cs.nyu.edu |
| Pseudocode | Yes | Algorithm 1 Quiet |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code for the methodology it describes. |
| Open Datasets | Yes | We used Art, Moebius, Shopvac, Flowers, and Umbrella images obtained from the Middlebury Stereo Datasets1; their sizes are 1390 1110, 1390 1110, 2356 1996, 2772 1980, 2880 1980, and 2960 2016, respectively. The six images are shown in Figure 1. 1http://vision.middlebury.edu/stereo/data/ |
| Dataset Splits | No | The paper uses standard benchmark datasets but does not provide specific training/validation/test splits, nor does it specify a cross-validation setup or random seed for splitting. |
| Hardware Specification | Yes | All experiments were conducted on a Linux 2.70 GHz Intel Xeon server. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In the experiments, we set the number of labels, K = 100, the number of iterations in each level, T = 50, the number of levels, B = 4, and the parameter of the Potts model, d = 20, by following the previous paper [Felzenszwalb and Huttenlocher, 2004]. |