Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection

Applied Sciences

Nadeem Anjum¹  Wajeeha Sultan¹  Mark Stansfield²  Naeem Ramzan² 

[1] Department of Computer Science, Capital University of Science and Technology, Islamabad Expressway, Kahuta Road Zone-V Sihala, Islamabad, Islamabad Capital Territory, Pakistan;School of Engineering and Computing, University of the West of Scotland, Technology Ave, Blantyre, Glasgow G72 0LH, UK;

关键词: deep-learning models;    salient-object detection;    hybrid architecture;    boundary-aware refinements;   

DOI  :  10.3390/app10238754

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown