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/Report/Conference proceedingChapters

1 Citation (Scopus)

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. Copyright © 2015 IEEE.
Original languageEnglish
Title of host publication2015 11th International Conference on Natural Computation (ICNC 2015)
EditorsZheng XIAO, Zhao TONG, Kenli II, Xingwei WANG, Keqin LI
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages154-158
ISBN (Electronic)9781467376785, 9781467376792
DOIs
Publication statusPublished - 2016

Citation

Hu, Y., Liang, Z., Chi, Z., & Fu, H. (2016). A combined convolutional neural network and potential region-of-interest model for saliency detection. In Z. Xiao, Z. Tong, K. Ii, X. Wang, & K. Li (Eds.), 2015 11th International Conference on Natural Computation (ICNC 2015) (pp. 154-158). Piscataway, NJ: IEEE.

Keywords

  • Saliency map
  • Saliency detection
  • Machine learning
  • Convolutional neural networks

Fingerprint Dive into the research topics of 'A combined convolutional neural network and potential region-of-interest model for saliency detection'. Together they form a unique fingerprint.