Hybrid local and global deep learning architecture for salient-object detection

Wajeeha Sultan, Nadeem Anjum*, Mark Stansfield, Naeem Ramzan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
5 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number8754
Number of pages15
JournalApplied Sciences
Volume10
Issue number23
DOIs
Publication statusPublished - 7 Dec 2020

Keywords

  • deep-learning models
  • salient-object detection
  • hybrid architecture
  • boundary-aware refinements

Fingerprint

Dive into the research topics of 'Hybrid local and global deep learning architecture for salient-object detection'. Together they form a unique fingerprint.

Cite this