Salient Object Detection with Semantic Priors
Authors: Tam V. Nguyen, Luoqi Liu
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further evaluate the proposed framework on two challenging datasets, namely, ECSSD and HKUIS. The extensive experimental results demonstrate that our method outperforms other state-of-the-art methods. |
| Researcher Affiliation | Academia | Tam V. Nguyen Department of Computer Science University of Dayton tamnguyen@udayton.edu Luoqi Liu Department of ECE National University of Singapore liuluoqi@u.nus.edu |
| Pseudocode | No | The paper describes the steps of the algorithm in text and provides a pipeline diagram (Figure 1), but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper mentions 'our unoptimized Matlab code' but does not provide a statement of release or a link to its source code. |
| Open Datasets | Yes | In particular, we utilize the CRF-FCN model trained from the PASCAL VOC 2007 dataset [Everingham et al., 2010] with 20 semantic classes. We trained our SP framework on HKUIS dataset [Li and Yu, 2015] (training part) which contains 4, 000 pairs of images and groundtruth maps. |
| Dataset Splits | No | The paper states that the HKUIS dataset has a 'training part' and a 'testing part', but does not explicitly describe a separate validation dataset split with specific percentages or sample counts. |
| Hardware Specification | Yes | The average time is taken on a PC with Intel i7 2.6 GHz CPU and 8GB RAM with our unoptimized Matlab code. |
| Software Dependencies | No | The paper mentions using 'CRF-FCN', 'random forest regressor', and 'Matlab code', but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For the implementation, we adopt the extension of FCN, namely CRF-FCN [Zheng et al., 2015], to perform the semantic segmentation for the input image. In particular, we utilize the CRF-FCN model trained from the PASCAL VOC 2007 dataset [Everingham et al., 2010] with 20 semantic classes. We trained our SP framework on HKUIS dataset [Li and Yu, 2015] (training part) which contains 4, 000 pairs of images and groundtruth maps. For the image over-segmentation, we adopt the method of [Achanta et al., 2012]. We set the number of regions as 200 as a trade-off between the fine over-segmentation and the processing time. |