A new approach to modeling the influence of image features on fixation selection in scenes

Antje Nuthmann, Wolfgang Einhäuser

Research output: Contribution to journalArticlepeer-review

Abstract

Which image characteristics predict where people fixate when memorizing natural images? To answer this question, we introduce a new analysis approach that combines a novel scene-patch analysis with generalized linear mixed models (GLMM). Our method allows for (1) directly describing the relationship between continuous feature value and fixation probability, and (2) assessing each feature’s unique contribution to fixation selection. To demonstrate this method, we estimated the relative contribution of various image features to fixation selection: luminance and luminance contrast (low-level features); edge density (a mid-level feature); visual clutter and image segmentation to approximate local object density in the scene (higher-level features). An additional predictor captured the central bias of fixation. The GLMM results revealed that edge density, clutter, and the number of homogenous segments in a patch can independently predict whether image patches are fixated or not. Importantly, neither luminance nor contrast had an independent effect above and beyond what could be accounted for by the other predictors. Since the parcellation of the scene and the selection of features can be tailored to the specific research question, our approach allows for assessing the interplay of various factors relevant for fixation selection in scenes in a powerful and flexible manner.
Original languageEnglish
Pages (from-to)82-96
Number of pages15
JournalAnnals of the New York Academy of Sciences
Volume1339
Issue number1
DOIs
Publication statusPublished - 1 Mar 2015
EventClosing Conference of the ZiF Research Group "Competition and priority control in mind and brain: new perspectives from task-driven vision" - Centre for Interdisciplinary Research (ZiF) at Bielefeld University, Bielefeld, Germany
Duration: 17 Mar 201421 Mar 2014

Keywords

  • naturalistic scenes
  • image features
  • eye movements
  • fixation probability
  • GLMM

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