Proto-Object Based Saliency Model with Second-Order Texture Feature

Takeshi Uejima, Ernst Niebur, Ralph Etienne-Cummings

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The nervous system can rapidly select important information from a visual scene and pay attention to it. Bottom-up saliency models use low-level features such as intensity, color, and orientation to generate a saliency map that predicts human fixations. Such algorithms work well for many images, however they miss the influence of texture. In this paper, we add a second-order texture channel to a proto-object based saliency model. The extended model shows significantly improved performance in predicting human fixations.

Original languageEnglish (US)
Title of host publication2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538636039
DOIs
Publication statusPublished - Dec 20 2018
Event2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Cleveland, United States
Duration: Oct 17 2018Oct 19 2018

Other

Other2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018
CountryUnited States
CityCleveland
Period10/17/1810/19/18

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Keywords

  • Gestalt
  • Proto-object
  • Saliency
  • Texture
  • Visual attention

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Health Informatics
  • Instrumentation
  • Signal Processing
  • Biomedical Engineering

Cite this

Uejima, T., Niebur, E., & Etienne-Cummings, R. (2018). Proto-Object Based Saliency Model with Second-Order Texture Feature. In 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings [8584749] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIOCAS.2018.8584749