Background prior-based salient object detection via deep reconstruction residual

Junwei Han, Dingwen Zhang, Xintao Hu, Lei Guo, Jinchang Ren, Feng Wu

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316 Citations (Scopus)
344 Downloads (Pure)


Detection of salient objects from images is gaining increasing research interest in recent years as it can substantially facilitate a wide range of content-based multimedia applications. Based on the assumption that foreground salient regions are distinctive within a certain context, most conventional approaches rely on a number of hand designed features and their distinctiveness measured using local or global contrast. Although these approaches have shown effective in dealing with simple images, their limited capability may cause difficulties when dealing with more complicated images. This paper proposes a novel framework for saliency detection by first modeling the background and then separating salient objects from the background. We develop stacked denoising autoencoders with deep learning architectures to model the background where latent patterns are explored and more powerful representations of data are learnt in an unsupervised and bottom up manner. Afterwards, we formulate the separation of salient objects from the background as a problem of measuring reconstruction residuals of deep autoencoders. Comprehensive evaluations on three benchmark datasets and comparisons with 9 state-of-the-art algorithms demonstrate the superiority of the proposed work.
Original languageEnglish
Pages (from-to)1309-1321
Number of pages13
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number8
Early online date18 Dec 2014
Publication statusPublished - Aug 2015


  • salient object detection
  • tacked denoising
  • background prior
  • deep reconstruction

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    Open Access
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