Salient Object Detection via Augmented Hypotheses
Authors: Tam Van Nguyen, Jose Sepulveda
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We finally evaluate the proposed framework on two challenging datasets, MSRA1000 and i Co Seg. Our extensive experimental results show that our method outperforms state-of-the-art approaches. |
| Researcher Affiliation | Academia | Tam V. Nguyen, Jose Sepulveda Department for Technology, Innovation and Enterprise Singapore Polytechnic {nguyen van tam, sepulveda jose}@sp.edu.sg |
| Pseudocode | Yes | Algorithm 1 Superpixel compactness computation |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code for the methodology is available. |
| Open Datasets | Yes | We evaluate and compare the performances of our algorithm against previous baseline algorithms on two representative benchmark datasets: the MSRA 1000 salient object dataset [Achanta et al., 2009] and the Interactive cosegmentation Dataset (i Co Seg) [Batra et al., 2010]. |
| Dataset Splits | No | The paper mentions evaluating precision/recall curves and adaptive thresholds, but does not explicitly describe train/validation/test dataset splits for their own model training. It mentions that the BING hypothesis generator was trained on VOC2007, but this is a component they use, not their own model's data split. |
| Hardware Specification | Yes | The average time of each method is measured on a PC with Intel i7 3.3 GHz CPU and 8GB RAM. |
| Software Dependencies | No | The paper mentions using BING, SLIC, C++, and MATLAB, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | BING hypothesis generator is trained with VOC2007 dataset [Everingham et al., 2010] same as in [Cheng et al., 2014]. In order to compute the foreground map, θ is set as 0.1 and we convert the color channels from RGB to Lab color space as suggested in [Achanta et al., 2009; Perazzi et al., 2012]. Regarding the image over-segmentation, we use SLIC [Achanta et al., 2012] for the superpixel segmentation. We set the number of superpixels as 100 as a trade-off between the fine oversegmentation and the processing time. |