Interactive Image Segmentation via Pairwise Likelihood Learning
Authors: Tao Wang, Quansen Sun, Qi Ge, Zexuan Ji, Qiang Chen, Guiyu Xia
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on challenging data sets demonstrate that the proposed method can obtain better performance than state-of-the-art methods.The proposed method was experimentally verified by comparing it with state-of-the-art approaches: Grab Cut [Rother et al., 2004], RW [Grady, 2006], LC [Casaca et al., 2014], SMRW [Dong et al., 2016] and NHO [Kim et al., 2010] on the Berkeley segmentation data set1 which contains 500 images with size 481 321 (or 321 481 ) and Microsoft Grab Cut database2 which contains 50 images with public seeds information. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanjing University of Science and Technology, China 2School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, China |
| Pseudocode | No | Not found. The method is described mathematically and textually, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | Not found. No explicit statement or link for open-source code is provided. |
| Open Datasets | Yes | The proposed method was experimentally verified by comparing it with state-of-the-art approaches: Grab Cut [Rother et al., 2004], RW [Grady, 2006], LC [Casaca et al., 2014], SMRW [Dong et al., 2016] and NHO [Kim et al., 2010] on the Berkeley segmentation data set1 which contains 500 images with size 481 321 (or 321 481 ) and Microsoft Grab Cut database2 which contains 50 images with public seeds information. 1http://www.eecs.berkeley.edu/Research/Projects/CS/vision/gr ouping/segbench/S. 2http://research.microsoft.com/enus/um/cambridge/projects/visi onimagevideoediting/segmentation/grabcut.htm |
| Dataset Splits | No | Not found. The paper mentions datasets but does not specify explicit training, validation, or test splits for reproducibility. |
| Hardware Specification | Yes | Table 3 lists the average running times of Grab Cut, RW, LC, SMRW, NHO and the proposed method on all 20 test images with size 321 481 in the Microsoft Grab Cut database on an Intel Xeon CPU running at 2.0 GHz in MATLAB. |
| Software Dependencies | No | Not found. The paper mentions |
| Experiment Setup | Yes | Parameters involved in the proposed scheme are set as follows: the constant is fixed as 60, the number of clusters F K and B K are both set to 3, the controlling parameter is set to 0.1, and the 4-neighborhood relationship of pixels is considered. |