Deep networks for saliency detection via local estimation and global search

This paper presents a saliency detection algorithm by integrating both local estimation and global search. In the local estimation stage, we detect local saliency by using a deep neural network (DNN-L) which learns local patch features to determine the saliency value of each pixel. The estimated local saliency maps are further refined by exploring the high level object concepts. In the global search stage, the local saliency map together with global contrast and geometric information are used as global features to describe a set of object candidate regions. Another deep neural network (DNN-G) is trained to predict the saliency score of each object region based on the global features. The final saliency map is…

• NS
  National Science Foundation

  Awards: 1149783, CAREER
• NN
  National Natural Science Foundation of China

  Award: 61472060
• FR
  Fundamental Research Funds for the Central Universities

  Awards: 61472060, DUT14YQ101