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Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection
| Content Provider | MDPI |
|---|---|
| Author | Sultan, Wajeeha Anjum, Nadeem Stansfield, Mark Ramzan, Naeem |
| Copyright Year | 2020 |
| Description | Salient-object detection is a fundamental and the most challenging problem in computer vision. This paper focuses on the detection of salient objects, especially in low-contrast images. To this end, a hybrid deep-learning architecture is proposed where features are extracted on both the local and global level. These features are then integrated to extract the exact boundary of the object of interest in an image. Experimentation was performed on five standard datasets, and results were compared with state-of-the-art approaches. Both qualitative and quantitative analyses showed the robustness of the proposed architecture. |
| Starting Page | 8754 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app10238754 |
| Journal | Applied Sciences |
| Issue Number | 23 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-12-07 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Artificial Intelligence Deep-learning Models Salient-object Detection Hybrid Architecture Boundary-aware Refinements |
| Content Type | Text |
| Resource Type | Article |
