Journal article
Improving semantic image segmentation with a probabilistic Superpixel-Based dense conditional random field
IEEE Access, Vol.6, pp.15297-15310
2018
Abstract
Deep convolutional neural networks (DCNNs) have been driving significant advances in semantic image segmentation due to their powerful feature representation for recognition. However, their performance in preserving object boundaries is still not satisfactory. Visual mechanism theory indicates that image segmentation tasks require not only recognition, like DCNNs, but also local visual attention capability. Considering that superpixel is good at grasping detailed local structure, we propose a probabilistic superpixel-based dense conditional random field model (PSP-CRF) to refine label assignments as a postprocessing optimization method. First, the well-known fully convolutional networks (FCN) and Deeplab-ResNet are employed to produce coarse prediction probabilistic maps at each pixel. Second, we construct a fully connected CRF model based on the PSP generated by the simple linear iterative clustering algorithm. In our approach, an effective refining algorithm with entropy is developed to convert the pixel-level appearance and position features to the normalized PSP, which works well for CRF. Third, our method optimizes the PSP-CRF to obtain the final label assignment results by employing a highly efficient mean field inference algorithm and some quadratic programming relaxation related algorithms. The experiments on the PASCAL VOC segmentation dataset demonstrate the effectiveness of our methods which can improve the segmentation performance of DCNNs to 82% in mIoU while increasing the computational efficiency by 47%.
Details
- Title
- Improving semantic image segmentation with a probabilistic Superpixel-Based dense conditional random field
- Authors/Creators
- L. Zhang (Author/Creator) - Xidian UniversityH. Li (Author/Creator) - Xidian UniversityP. Shen (Author/Creator) - Xidian UniversityG. Zhu (Author/Creator) - Xidian UniversityJ. Song (Author/Creator) - Xidian UniversityS.A.A. Shah (Author/Creator) - The University of Western AustraliaM. Bennamoun (Author/Creator) - The University of Western Australia
- Publication Details
- IEEE Access, Vol.6, pp.15297-15310
- Publisher
- IEEE
- Identifiers
- 991005545175707891
- Copyright
- © 2019 IEEE
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.128 Deep Visual Recognition
- Web Of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic
- Telecommunications
- ESI research areas
- Engineering