Introducing context-dependent and spatially-variant viewing biases in saccadic models

Olivier Le Meur 1 Antoine Coutrot 2
1 Sirocco - Analysis representation, compression and communication of visual data
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Previous research showed the existence of systematic tendencies in viewing behavior during scene exploration. For instance, saccades are known to follow a positively skewed, long-tailed distribution, and to be more frequently initiated in the horizontal or vertical directions. In this study, we hypothesize that these viewing biases are not universal, but are modulated by the semantic visual category of the stimulus. We show that the joint distribution of saccade amplitudes and orientations significantly varies from one visual category to another. These joint distributions are in addition spatially variant within the scene frame. We demonstrate that a saliency model based on this better understanding of viewing behavioral biases and blind to any visual information outperforms well-established saliency models. We also propose a saccadic model that takes into account classical low-level features and spatially-variant and context-dependent viewing biases. This model outperforms state-of-the-art saliency models, and provides scanpaths in close agreement with human behavior. The better description of viewing biases will not only improve current models of visual attention but could also influence many other applications such as the design of human–computer interfaces, patient diagnosis or image/video processing applications.
Type de document :
Article dans une revue
Vision Research, Elsevier, 2016, 121, pp.72 - 84. 〈10.1016/j.visres.2016.01.005〉
Liste complète des métadonnées

Littérature citée [70 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01391745
Contributeur : Olivier Le Meur <>
Soumis le : jeudi 3 novembre 2016 - 18:01:54
Dernière modification le : mercredi 16 mai 2018 - 11:23:38
Document(s) archivé(s) le : samedi 4 février 2017 - 14:19:26

Fichier

LeMeur_SystematicTendencies_V3...
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Olivier Le Meur, Antoine Coutrot. Introducing context-dependent and spatially-variant viewing biases in saccadic models. Vision Research, Elsevier, 2016, 121, pp.72 - 84. 〈10.1016/j.visres.2016.01.005〉. 〈hal-01391745〉

Partager

Métriques

Consultations de la notice

209

Téléchargements de fichiers

103