Literature DB >> 33488689

Inherent Importance of Early Visual Features in Attraction of Human Attention.

Reza Eghdam1,2, Reza Ebrahimpour1,2, Iman Zabbah3, Sajjad Zabbah2.   

Abstract

Local contrasts attract human attention to different areas of an image. Studies have shown that orientation, color, and intensity are some basic visual features which their contrasts attract our attention. Since these features are in different modalities, their contribution in the attraction of human attention is not easily comparable. In this study, we investigated the importance of these three features in the attraction of human attention in synthetic and natural images. Choosing 100% percent detectable contrast in each modality, we studied the competition between different features. Psychophysics results showed that, although single features can be detected easily in all trials, when features were presented simultaneously in a stimulus, orientation always attracts subject's attention. In addition, computational results showed that orientation feature map is more informative about the pattern of human saccades in natural images. Finally, using optimization algorithms we quantified the impact of each feature map in construction of the final saliency map.
Copyright © 2020 Reza Eghdam et al.

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Year:  2020        PMID: 33488689      PMCID: PMC7803287          DOI: 10.1155/2020/3496432

Source DB:  PubMed          Journal:  Comput Intell Neurosci


  19 in total

1.  Modeling global scene factors in attention.

Authors:  Antonio Torralba
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  2003-07       Impact factor: 2.129

2.  State-of-the-art in visual attention modeling.

Authors:  Ali Borji; Laurent Itti
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2013-01       Impact factor: 6.226

3.  Saliency, attention, and visual search: an information theoretic approach.

Authors:  Neil D B Bruce; John K Tsotsos
Journal:  J Vis       Date:  2009-03-13       Impact factor: 2.240

4.  Learning a saliency map using fixated locations in natural scenes.

Authors:  Qi Zhao; Christof Koch
Journal:  J Vis       Date:  2011-03-10       Impact factor: 2.240

5.  The role of features in preattentive vision: comparison of orientation, motion and color cues.

Authors:  H C Nothdurft
Journal:  Vision Res       Date:  1993-09       Impact factor: 1.886

Review 6.  Computational modelling of visual attention.

Authors:  L Itti; C Koch
Journal:  Nat Rev Neurosci       Date:  2001-03       Impact factor: 34.870

7.  Visual Saliency Detection Based on Multiscale Deep CNN Features.

Authors: 
Journal:  IEEE Trans Image Process       Date:  2016-08-24       Impact factor: 10.856

8.  Combined contributions of feedforward and feedback inputs to bottom-up attention.

Authors:  Peyman Khorsand; Tirin Moore; Alireza Soltani
Journal:  Front Psychol       Date:  2015-03-02

9.  Feature singletons attract spatial attention independently of feature priming.

Authors:  Amit Yashar; Alex L White; Wanghaoming Fang; Marisa Carrasco
Journal:  J Vis       Date:  2017-08-01       Impact factor: 2.240

10.  Unified Saliency Detection Model Using Color and Texture Features.

Authors:  Libo Zhang; Lin Yang; Tiejian Luo
Journal:  PLoS One       Date:  2016-02-18       Impact factor: 3.240

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  1 in total

1.  Stable Median Centre Clustering for Unsupervised Domain Adaptation Person Re-Identification.

Authors:  Jifeng Guo; Wenbo Sun; Zhiqi Pang; Yuxiao Fei; Yu Chen
Journal:  Comput Intell Neurosci       Date:  2021-07-21
  1 in total

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