Abstract
The human visual system (HVS) has evolved to have the ability to selectively focus on the most relevant parts of a visual scene. This mechanism, referred to as visual attention (VA), has been the focus of several neurological and psychological studies in the past few decades. These studies have inspired several computational VA models which have been successfully applied to problems in computer vision and robotics. In this paper we provide a comprehensive survey of the state-of-the-art in computational VA modeling with a special focus on the latest trends. We review several models published since 2012. We also discuss theoretical advantages and disadvantages of each approach. In addition, we describe existing methodologies to evaluate computational models through the use of eye-tracking data along with the VA performance metrics used. We also discuss shortcomings in existing approaches and describe approaches to overcome these shortcomings. A recent subjective evaluation for benchmarking existing VA metrics is also presented and open problems in VA are discussed.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 347-427 |
| Number of pages | 81 |
| Journal | Foundations and Trends in Signal Processing |
| Volume | 10 |
| Issue number | 4 |
| DOIs | |
| State | Published - 2017 |
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ASJC Scopus subject areas
- Signal Processing
Cite this
Computational visual attention models. / Gide, Milind S.; Karam, Lina.
In: Foundations and Trends in Signal Processing, Vol. 10, No. 4, 2017, p. 347-427.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Computational visual attention models
AU - Gide, Milind S.
AU - Karam, Lina
PY - 2017
Y1 - 2017
N2 - The human visual system (HVS) has evolved to have the ability to selectively focus on the most relevant parts of a visual scene. This mechanism, referred to as visual attention (VA), has been the focus of several neurological and psychological studies in the past few decades. These studies have inspired several computational VA models which have been successfully applied to problems in computer vision and robotics. In this paper we provide a comprehensive survey of the state-of-the-art in computational VA modeling with a special focus on the latest trends. We review several models published since 2012. We also discuss theoretical advantages and disadvantages of each approach. In addition, we describe existing methodologies to evaluate computational models through the use of eye-tracking data along with the VA performance metrics used. We also discuss shortcomings in existing approaches and describe approaches to overcome these shortcomings. A recent subjective evaluation for benchmarking existing VA metrics is also presented and open problems in VA are discussed.
AB - The human visual system (HVS) has evolved to have the ability to selectively focus on the most relevant parts of a visual scene. This mechanism, referred to as visual attention (VA), has been the focus of several neurological and psychological studies in the past few decades. These studies have inspired several computational VA models which have been successfully applied to problems in computer vision and robotics. In this paper we provide a comprehensive survey of the state-of-the-art in computational VA modeling with a special focus on the latest trends. We review several models published since 2012. We also discuss theoretical advantages and disadvantages of each approach. In addition, we describe existing methodologies to evaluate computational models through the use of eye-tracking data along with the VA performance metrics used. We also discuss shortcomings in existing approaches and describe approaches to overcome these shortcomings. A recent subjective evaluation for benchmarking existing VA metrics is also presented and open problems in VA are discussed.
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U2 - 10.1561/2000000055
DO - 10.1561/2000000055
M3 - Article
VL - 10
SP - 347
EP - 427
JO - Foundations and Trends in Signal Processing
JF - Foundations and Trends in Signal Processing
SN - 1932-8346
IS - 4
ER -