Semantic Segmentation Using Multiple Graphs with Block-Diagonal Constraints
Authors: Ke Zhang, Wei Zhang, Sheng Zeng, Xiangyang Xue
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two real-world image datasets demonstrate the effectiveness of our method. We conduct the experiments on two real-world image datasets MSRC (Shotton et al. 2006) and VOC2007 (Everingham et al. 2007). |
| Researcher Affiliation | Academia | Ke Zhang, Wei Zhang, Sheng Zeng, Xiangyang Xue Shanghai Engineering Research Center for Video Technology and System School of Computer Science, Fudan University, China {k zhang,weizh,zengsheng,xyxue}@fudan.edu.cn |
| Pseudocode | No | The paper describes the optimization steps using equations and textual explanations, but it does not provide a formal pseudocode or algorithm block labeled as such. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code of the described methodology. |
| Open Datasets | Yes | We conduct the experiments on two real-world image datasets MSRC (Shotton et al. 2006) and VOC2007 (Everingham et al. 2007). |
| Dataset Splits | Yes | The training, validation and test subsets are 45%, 10%, and 45% of the whole image dataset, respectively. (for MSRC-21) and we conduct experiments on the segmentation set with the train-val split including 422 training-validation images and 210 test images (for VOC2007). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions using MOSEK as a solver ('MOSEK: http://www.mosek.com') but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Parameters α, β, γ are set by 10-fold cross-validation on the training set of each dataset for different segmentations. To get results from different quantization of images, 9 sets of parameters of the mean-shift kernels were randomly chosen as (5;5); (5;7); (5;9); (8;7); (8;9.5); (8;11); (12;10); (12; 15); (12;18). |