Investigating a space-variant weighted salience account of visual selection

BT Vincent; T Troscianko; ID Gilchrist

doi:10.1016/j.visres.2007.02.014

Investigating a space-variant weighted salience account of visual selection

BT Vincent, T Troscianko, ID Gilchrist

Research output: Contribution to journal › Article (Academic Journal) › peer-review

21 Citations (Scopus)

Abstract

Weighted salience models are a popular framework for image-driven visual attentional processes. These models operate by: sampling the visual environment; calculating feature maps; combining them in a weighted sum and using this to determine where the eye will fixate next. We examine these stages in turn. We find that a biologically plausible non-uniform retinal sampling causes feature coding unreliability. The linear weighted sum operation seems an adequate model if statistical feature dimension dependencies are considered. Using signal detection theory we find good discrimination between targets and non-targets in the weighted sum, but the fixation criterion of 'peak salience' is suboptimal. (c) 2007 Elsevier Ltd. All rights reserved.

Translated title of the contribution	Investigating a space-variant weighted salience account of visual selection
Original language	English
Pages (from-to)	1809 - 1820
Number of pages	12
Journal	Vision Research
Volume	47
DOIs	https://doi.org/10.1016/j.visres.2007.02.014
Publication status	Published - Jun 2007

Access to Document

10.1016/j.visres.2007.02.014

Handle.net

Persistent link

Cite this

@article{ac5d09168ae4452380f7e14337e36fe7,

title = "Investigating a space-variant weighted salience account of visual selection",

abstract = "Weighted salience models are a popular framework for image-driven visual attentional processes. These models operate by: sampling the visual environment; calculating feature maps; combining them in a weighted sum and using this to determine where the eye will fixate next. We examine these stages in turn. We find that a biologically plausible non-uniform retinal sampling causes feature coding unreliability. The linear weighted sum operation seems an adequate model if statistical feature dimension dependencies are considered. Using signal detection theory we find good discrimination between targets and non-targets in the weighted sum, but the fixation criterion of 'peak salience' is suboptimal. (c) 2007 Elsevier Ltd. All rights reserved.",

author = "BT Vincent and T Troscianko and ID Gilchrist",

year = "2007",

month = jun,

doi = "10.1016/j.visres.2007.02.014",

language = "English",

volume = "47",

pages = "1809 -- 1820",

journal = "Vision Research",

issn = "0042-6989",

publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Investigating a space-variant weighted salience account of visual selection

AU - Vincent, BT

AU - Troscianko, T

AU - Gilchrist, ID

PY - 2007/6

Y1 - 2007/6

N2 - Weighted salience models are a popular framework for image-driven visual attentional processes. These models operate by: sampling the visual environment; calculating feature maps; combining them in a weighted sum and using this to determine where the eye will fixate next. We examine these stages in turn. We find that a biologically plausible non-uniform retinal sampling causes feature coding unreliability. The linear weighted sum operation seems an adequate model if statistical feature dimension dependencies are considered. Using signal detection theory we find good discrimination between targets and non-targets in the weighted sum, but the fixation criterion of 'peak salience' is suboptimal. (c) 2007 Elsevier Ltd. All rights reserved.

AB - Weighted salience models are a popular framework for image-driven visual attentional processes. These models operate by: sampling the visual environment; calculating feature maps; combining them in a weighted sum and using this to determine where the eye will fixate next. We examine these stages in turn. We find that a biologically plausible non-uniform retinal sampling causes feature coding unreliability. The linear weighted sum operation seems an adequate model if statistical feature dimension dependencies are considered. Using signal detection theory we find good discrimination between targets and non-targets in the weighted sum, but the fixation criterion of 'peak salience' is suboptimal. (c) 2007 Elsevier Ltd. All rights reserved.

U2 - 10.1016/j.visres.2007.02.014

DO - 10.1016/j.visres.2007.02.014

M3 - Article (Academic Journal)

VL - 47

SP - 1809

EP - 1820

JO - Vision Research

JF - Vision Research

SN - 0042-6989

ER -

Investigating a space-variant weighted salience account of visual selection

Abstract

Access to Document

Handle.net

Fingerprint

Cite this