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

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 (GLMMs). 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

SUBMITTER: Nuthmann A 

PROVIDER: S-EPMC4402003 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature