Abstract
A saliency detection model for approaching the human performance is a challenging research topic. In this paper, a new saliency model is proposed to detect saliency in natural scenes by using a trained convolutional neural network and a region-based validation method. The convolutional neural network (CNN) focuses on image details and local contrast of an image, while the region-based validation method focus on global information. Experimental results show that the two components of the model are complementary for each other in producing high-quality saliency maps.
Original language | English |
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Title of host publication | 2015 11th International Conference on Natural Computation, ICNC 2015 |
Publisher | IEEE Computer Society |
Pages | 154-158 |
Number of pages | 5 |
Volume | 2016-January |
ISBN (Electronic) | 9781467376792 |
DOIs | |
Publication status | Published - 8 Jan 2016 |
Event | 11th International Conference on Natural Computation, ICNC 2015 - Zhangjiajie, China Duration: 15 Aug 2015 → 17 Aug 2015 |
Conference
Conference | 11th International Conference on Natural Computation, ICNC 2015 |
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Country | China |
City | Zhangjiajie |
Period | 15/08/15 → 17/08/15 |
Keywords
- Convolutional neural networks
- machine learning
- saliency detection
- saliency map
ASJC Scopus subject areas
- Computer Science(all)
- Biomedical Engineering
- Computational Mechanics
- Mathematics(all)
- Neuroscience(all)