Visual Saliency Prediction Using a Mixture of Deep Neural Networks

Samuel Dodge, Lina Karam

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Visual saliency models have recently begun to incorporate deep learning to achieve predictive capacity much greater than previous unsupervised methods. However, most existing models predict saliency without explicit knowledge of global scene semantic information. We propose a model (MxSalNet) that incorporates global scene semantic information in addition to local information gathered by a convolutional neural network. Our model is formulated as a mixture of experts. Each expert network is trained to predict saliency for a set of closely related images. The final saliency map is computed as a weighted mixture of the expert networks' output, with weights determined by a separate gating network. This gating network is guided by global scene information to predict weights. The expert networks and the gating network are trained simultaneously in an end-to-end manner. We show that our mixture formulation leads to improvement in performance over an otherwise identical non-mixture model that does not incorporate global scene information. Additionally, we show that our model achieves better performance than several other visual saliency models.

Original languageEnglish (US)
Pages (from-to)4080-4090
Number of pages11
JournalIEEE Transactions on Image Processing
Volume27
Issue number8
DOIs
StatePublished - Aug 2018

Keywords

  • Visual attention
  • deep learning
  • human visual system
  • saliency map

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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