A combined convolutional neural network and potential region-of-interest model for saliency detection

Yu Hu, Zhen Liang, Zheru Chi, Hong Fu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

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 languageEnglish
Title of host publication2015 11th International Conference on Natural Computation, ICNC 2015
PublisherIEEE Computer Society
Pages154-158
Number of pages5
Volume2016-January
ISBN (Electronic)9781467376792
DOIs
Publication statusPublished - 8 Jan 2016
Event11th International Conference on Natural Computation, ICNC 2015 - Zhangjiajie, China
Duration: 15 Aug 201517 Aug 2015

Conference

Conference11th International Conference on Natural Computation, ICNC 2015
CountryChina
CityZhangjiajie
Period15/08/1517/08/15

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)

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