Cargando…
A new approach to modeling the influence of image features on fixation selection in scenes
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 relationshi...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BlackWell Publishing Ltd
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4402003/ https://www.ncbi.nlm.nih.gov/pubmed/25752239 http://dx.doi.org/10.1111/nyas.12705 |
_version_ | 1782367215448752128 |
---|---|
author | Nuthmann, Antje Einhäuser, Wolfgang |
author_facet | Nuthmann, Antje Einhäuser, Wolfgang |
author_sort | Nuthmann, Antje |
collection | PubMed |
description | 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 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. |
format | Online Article Text |
id | pubmed-4402003 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BlackWell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-44020032015-04-22 A new approach to modeling the influence of image features on fixation selection in scenes Nuthmann, Antje Einhäuser, Wolfgang Ann N Y Acad Sci Original Articles 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 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. BlackWell Publishing Ltd 2015-03 2015-03-09 /pmc/articles/PMC4402003/ /pubmed/25752239 http://dx.doi.org/10.1111/nyas.12705 Text en © 2015 The New York Academy of Sciences http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
spellingShingle | Original Articles Nuthmann, Antje Einhäuser, Wolfgang A new approach to modeling the influence of image features on fixation selection in scenes |
title | A new approach to modeling the influence of image features on fixation selection in scenes |
title_full | A new approach to modeling the influence of image features on fixation selection in scenes |
title_fullStr | A new approach to modeling the influence of image features on fixation selection in scenes |
title_full_unstemmed | A new approach to modeling the influence of image features on fixation selection in scenes |
title_short | A new approach to modeling the influence of image features on fixation selection in scenes |
title_sort | new approach to modeling the influence of image features on fixation selection in scenes |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4402003/ https://www.ncbi.nlm.nih.gov/pubmed/25752239 http://dx.doi.org/10.1111/nyas.12705 |
work_keys_str_mv | AT nuthmannantje anewapproachtomodelingtheinfluenceofimagefeaturesonfixationselectioninscenes AT einhauserwolfgang anewapproachtomodelingtheinfluenceofimagefeaturesonfixationselectioninscenes AT nuthmannantje newapproachtomodelingtheinfluenceofimagefeaturesonfixationselectioninscenes AT einhauserwolfgang newapproachtomodelingtheinfluenceofimagefeaturesonfixationselectioninscenes |