Saliency prediction based on object recognition and gaze analysis

Tomoki Ishikawa, Takahiro Yakoh

Research output: Contribution to journalArticlepeer-review

Abstract

Predicting the human visual attention in an image is called saliency prediction and is an active research area in the field of neuroscience and computer vision. Early works on saliency prediction was performed by using low-level features. In recent years, convolutional neural networks have been adapted for saliency prediction and achieved the state-of-the-art performance. However, the eye-gaze depends on the personality of each viewer and conventional methods did not take into account such individual properties of the viewer. Therefore, this paper proposes a novel saliency prediction method considering the influence of eye-gaze. Assuming that personality can be expressed as the degree of attention to an object, our proposed method considers the personality by learning which objects are likely to be perceived by each viewer and weighting the universal saliency map with the generated mask based on the object detection results. The experimental results show that the proposed universal saliency map achieves higher accuracy than conventional methods on the public dataset, and the proposed weighted saliency map can reflect the variation of the eye-gaze influences among viewers.

Original languageEnglish
JournalElectronics and Communications in Japan
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • convolutional neural network
  • gaze analysis
  • object recognition
  • saliency map

ASJC Scopus subject areas

  • Signal Processing
  • Physics and Astronomy(all)
  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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