Learning to detect saliency with deep structure

Yu HU, Zenghai CHEN, Zheru CHI, Hong FU

Research output: Chapter in Book/Report/Conference proceedingChapters

4 Citations (Scopus)

Abstract

Deep learning has shown great successes in solving various problems of computer vision. To the best of our knowledge, however, little existing work applies deep learning to saliency modeling. In this paper, a new saliency model based on convolutional neural network is proposed. The proposed model is able to produce a saliency map directly from an image's pixels. In the model, multi-level output values are adopted to simulate continuous values in a saliency map. Differing from most neural networks that use a relatively small number of output nodes, the output layer of our model has a large number of nodes. To make the training more efficient, an improved learning algorithm is adopted to train the model. Experimental results show that the proposed model succeeds in generating acceptable saliency maps after proper training. Copyright © 2015 IEEE.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2015)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1770-1775
ISBN (Electronic)9781479986972, 9781479986965
ISBN (Print)9781479986989
DOIs
Publication statusPublished - 2016

Citation

Hu, Y., Chen, Z., Chi, Z., & Fu, H. (2016). Learning to detect saliency with deep structure. In 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2015) (pp. 1770-1775). Piscataway, NJ: IEEE.

Keywords

  • Saliency detection
  • Saliency map
  • Deep learning
  • Convolutional neural network

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