Emergence of visual saliency from natural scenes via context-mediated probability distributions coding.

Visual saliency is the perceptual quality that makes some items in visual scenes stand out from their immediate contexts. Visual saliency plays important roles in natural vision in that saliency can direct eye movements, deploy attention, and facilitate tasks like object detection and scene understa...

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Autores principales:	Jinhua Xu, Zhiyong Yang, Joe Z Tsien
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/45a29db07c664610b4eaf7814a558ef4
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spelling	oai:doaj.org-article:45a29db07c664610b4eaf7814a558ef42021-11-18T07:01:01ZEmergence of visual saliency from natural scenes via context-mediated probability distributions coding.1932-620310.1371/journal.pone.0015796https://doaj.org/article/45a29db07c664610b4eaf7814a558ef42010-12-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21209963/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Visual saliency is the perceptual quality that makes some items in visual scenes stand out from their immediate contexts. Visual saliency plays important roles in natural vision in that saliency can direct eye movements, deploy attention, and facilitate tasks like object detection and scene understanding. A central unsolved issue is: What features should be encoded in the early visual cortex for detecting salient features in natural scenes? To explore this important issue, we propose a hypothesis that visual saliency is based on efficient encoding of the probability distributions (PDs) of visual variables in specific contexts in natural scenes, referred to as context-mediated PDs in natural scenes. In this concept, computational units in the model of the early visual system do not act as feature detectors but rather as estimators of the context-mediated PDs of a full range of visual variables in natural scenes, which directly give rise to a measure of visual saliency of any input stimulus. To test this hypothesis, we developed a model of the context-mediated PDs in natural scenes using a modified algorithm for independent component analysis (ICA) and derived a measure of visual saliency based on these PDs estimated from a set of natural scenes. We demonstrated that visual saliency based on the context-mediated PDs in natural scenes effectively predicts human gaze in free-viewing of both static and dynamic natural scenes. This study suggests that the computation based on the context-mediated PDs of visual variables in natural scenes may underlie the neural mechanism in the early visual cortex for detecting salient features in natural scenes.Jinhua XuZhiyong YangJoe Z TsienPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 12, p e15796 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jinhua Xu Zhiyong Yang Joe Z Tsien Emergence of visual saliency from natural scenes via context-mediated probability distributions coding.
description	Visual saliency is the perceptual quality that makes some items in visual scenes stand out from their immediate contexts. Visual saliency plays important roles in natural vision in that saliency can direct eye movements, deploy attention, and facilitate tasks like object detection and scene understanding. A central unsolved issue is: What features should be encoded in the early visual cortex for detecting salient features in natural scenes? To explore this important issue, we propose a hypothesis that visual saliency is based on efficient encoding of the probability distributions (PDs) of visual variables in specific contexts in natural scenes, referred to as context-mediated PDs in natural scenes. In this concept, computational units in the model of the early visual system do not act as feature detectors but rather as estimators of the context-mediated PDs of a full range of visual variables in natural scenes, which directly give rise to a measure of visual saliency of any input stimulus. To test this hypothesis, we developed a model of the context-mediated PDs in natural scenes using a modified algorithm for independent component analysis (ICA) and derived a measure of visual saliency based on these PDs estimated from a set of natural scenes. We demonstrated that visual saliency based on the context-mediated PDs in natural scenes effectively predicts human gaze in free-viewing of both static and dynamic natural scenes. This study suggests that the computation based on the context-mediated PDs of visual variables in natural scenes may underlie the neural mechanism in the early visual cortex for detecting salient features in natural scenes.
format	article
author	Jinhua Xu Zhiyong Yang Joe Z Tsien
author_facet	Jinhua Xu Zhiyong Yang Joe Z Tsien
author_sort	Jinhua Xu
title	Emergence of visual saliency from natural scenes via context-mediated probability distributions coding.
title_short	Emergence of visual saliency from natural scenes via context-mediated probability distributions coding.
title_full	Emergence of visual saliency from natural scenes via context-mediated probability distributions coding.
title_fullStr	Emergence of visual saliency from natural scenes via context-mediated probability distributions coding.
title_full_unstemmed	Emergence of visual saliency from natural scenes via context-mediated probability distributions coding.
title_sort	emergence of visual saliency from natural scenes via context-mediated probability distributions coding.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/45a29db07c664610b4eaf7814a558ef4
work_keys_str_mv	AT jinhuaxu emergenceofvisualsaliencyfromnaturalscenesviacontextmediatedprobabilitydistributionscoding AT zhiyongyang emergenceofvisualsaliencyfromnaturalscenesviacontextmediatedprobabilitydistributionscoding AT joeztsien emergenceofvisualsaliencyfromnaturalscenesviacontextmediatedprobabilitydistributionscoding
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Emergence of visual saliency from natural scenes via context-mediated probability distributions coding.

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