Visual attention model with a novel learning strategy and its application to target detection from SAR images

Fei Gao, Xiangshang Xue, Jun Wang, Jinping Sun, Amir Hussain, Erfu Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

The selective visual attention mechanism in human visual system helps human to act efficiently when dealing with massive visual information. Over the last two decades, biologically inspired attention model has drawn lots of research attention and many models have been proposed. However, the top-down cues in human brain are still not fully understood, which makes top-down models not biologically plausible. This paper proposes an attention model containing both the bottom-up stage and top-down stage for the target detection from SAR (Synthetic Aperture Radar) images. The bottom-up stage is based on the biologically-inspired Itti model and is modified by taking fully into account the characteristic of SAR images. The top-down stage contains a novel learning strategy to make the full use of prior information. It is an extension of the bottom-up process and more biologically plausible. The experiments in this research aim to detect vehicles in different scenes to validate the proposed model by comparing with the well-known CFAR (constant false alarm rate) algorithm.

LanguageEnglish
Title of host publicationAdvances in Brain Inspired Cognitive Systems
Subtitle of host publication8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings
EditorsCheng-Lin Liu, Amir Hussain, Bin Luo, Kay Chen Tan, Yi Zeng, Zhaoxiang Zhang
Place of PublicationCham, Switzerland
PublisherSpringer-Verlag
Pages149-160
Number of pages12
ISBN (Print)9783319496849
DOIs
Publication statusPublished - 13 Nov 2016
Event8th International Conference on Brain Inspired Cognitive Systems, BICS 2016 - Beijing, China
Duration: 28 Nov 201630 Nov 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10023
ISSN (Print)0302 9743

Conference

Conference8th International Conference on Brain Inspired Cognitive Systems, BICS 2016
CountryChina
CityBeijing
Period28/11/1630/11/16

Fingerprint

Visual Attention
Learning Strategies
Target Detection
Synthetic Aperture
Synthetic aperture radar
Target tracking
Radar
Bottom-up
Model
Human Visual System
False Alarm Rate
Prior Information
Brain
Experiment
Experiments

Keywords

  • learning strategy
  • object detection
  • Synthetic Aperture Radar (SAR) images
  • visual attention model
  • visual information
  • bottom-up
  • top-down
  • constant false alarm rate algorithm

Cite this

Gao, F., Xue, X., Wang, J., Sun, J., Hussain, A., & Yang, E. (2016). Visual attention model with a novel learning strategy and its application to target detection from SAR images. In C-L. Liu, A. Hussain, B. Luo, K. C. Tan, Y. Zeng, & Z. Zhang (Eds.), Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings (pp. 149-160). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10023). Cham, Switzerland: Springer-Verlag. https://doi.org/10.1007/978-3-319-49685-6_14
Gao, Fei ; Xue, Xiangshang ; Wang, Jun ; Sun, Jinping ; Hussain, Amir ; Yang, Erfu. / Visual attention model with a novel learning strategy and its application to target detection from SAR images. Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings. editor / Cheng-Lin Liu ; Amir Hussain ; Bin Luo ; Kay Chen Tan ; Yi Zeng ; Zhaoxiang Zhang. Cham, Switzerland : Springer-Verlag, 2016. pp. 149-160 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{943f9f6318574de9b3d6147958aa991b,
title = "Visual attention model with a novel learning strategy and its application to target detection from SAR images",
abstract = "The selective visual attention mechanism in human visual system helps human to act efficiently when dealing with massive visual information. Over the last two decades, biologically inspired attention model has drawn lots of research attention and many models have been proposed. However, the top-down cues in human brain are still not fully understood, which makes top-down models not biologically plausible. This paper proposes an attention model containing both the bottom-up stage and top-down stage for the target detection from SAR (Synthetic Aperture Radar) images. The bottom-up stage is based on the biologically-inspired Itti model and is modified by taking fully into account the characteristic of SAR images. The top-down stage contains a novel learning strategy to make the full use of prior information. It is an extension of the bottom-up process and more biologically plausible. The experiments in this research aim to detect vehicles in different scenes to validate the proposed model by comparing with the well-known CFAR (constant false alarm rate) algorithm.",
keywords = "learning strategy, object detection, Synthetic Aperture Radar (SAR) images, visual attention model, visual information, bottom-up, top-down, constant false alarm rate algorithm",
author = "Fei Gao and Xiangshang Xue and Jun Wang and Jinping Sun and Amir Hussain and Erfu Yang",
note = "The final publication is available at link.springer.com",
year = "2016",
month = "11",
day = "13",
doi = "10.1007/978-3-319-49685-6_14",
language = "English",
isbn = "9783319496849",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "149--160",
editor = "Cheng-Lin Liu and Amir Hussain and Bin Luo and Tan, {Kay Chen} and Yi Zeng and Zhaoxiang Zhang",
booktitle = "Advances in Brain Inspired Cognitive Systems",

}

Gao, F, Xue, X, Wang, J, Sun, J, Hussain, A & Yang, E 2016, Visual attention model with a novel learning strategy and its application to target detection from SAR images. in C-L Liu, A Hussain, B Luo, KC Tan, Y Zeng & Z Zhang (eds), Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10023, Springer-Verlag, Cham, Switzerland, pp. 149-160, 8th International Conference on Brain Inspired Cognitive Systems, BICS 2016, Beijing, China, 28/11/16. https://doi.org/10.1007/978-3-319-49685-6_14

Visual attention model with a novel learning strategy and its application to target detection from SAR images. / Gao, Fei; Xue, Xiangshang; Wang, Jun; Sun, Jinping; Hussain, Amir; Yang, Erfu.

Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings. ed. / Cheng-Lin Liu; Amir Hussain; Bin Luo; Kay Chen Tan; Yi Zeng; Zhaoxiang Zhang. Cham, Switzerland : Springer-Verlag, 2016. p. 149-160 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10023).

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - Visual attention model with a novel learning strategy and its application to target detection from SAR images

AU - Gao, Fei

AU - Xue, Xiangshang

AU - Wang, Jun

AU - Sun, Jinping

AU - Hussain, Amir

AU - Yang, Erfu

N1 - The final publication is available at link.springer.com

PY - 2016/11/13

Y1 - 2016/11/13

N2 - The selective visual attention mechanism in human visual system helps human to act efficiently when dealing with massive visual information. Over the last two decades, biologically inspired attention model has drawn lots of research attention and many models have been proposed. However, the top-down cues in human brain are still not fully understood, which makes top-down models not biologically plausible. This paper proposes an attention model containing both the bottom-up stage and top-down stage for the target detection from SAR (Synthetic Aperture Radar) images. The bottom-up stage is based on the biologically-inspired Itti model and is modified by taking fully into account the characteristic of SAR images. The top-down stage contains a novel learning strategy to make the full use of prior information. It is an extension of the bottom-up process and more biologically plausible. The experiments in this research aim to detect vehicles in different scenes to validate the proposed model by comparing with the well-known CFAR (constant false alarm rate) algorithm.

AB - The selective visual attention mechanism in human visual system helps human to act efficiently when dealing with massive visual information. Over the last two decades, biologically inspired attention model has drawn lots of research attention and many models have been proposed. However, the top-down cues in human brain are still not fully understood, which makes top-down models not biologically plausible. This paper proposes an attention model containing both the bottom-up stage and top-down stage for the target detection from SAR (Synthetic Aperture Radar) images. The bottom-up stage is based on the biologically-inspired Itti model and is modified by taking fully into account the characteristic of SAR images. The top-down stage contains a novel learning strategy to make the full use of prior information. It is an extension of the bottom-up process and more biologically plausible. The experiments in this research aim to detect vehicles in different scenes to validate the proposed model by comparing with the well-known CFAR (constant false alarm rate) algorithm.

KW - learning strategy

KW - object detection

KW - Synthetic Aperture Radar (SAR) images

KW - visual attention model

KW - visual information

KW - bottom-up

KW - top-down

KW - constant false alarm rate algorithm

UR - http://www.scopus.com/inward/record.url?scp=84997235981&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-49685-6_14

DO - 10.1007/978-3-319-49685-6_14

M3 - Conference contribution book

SN - 9783319496849

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 149

EP - 160

BT - Advances in Brain Inspired Cognitive Systems

A2 - Liu, Cheng-Lin

A2 - Hussain, Amir

A2 - Luo, Bin

A2 - Tan, Kay Chen

A2 - Zeng, Yi

A2 - Zhang, Zhaoxiang

PB - Springer-Verlag

CY - Cham, Switzerland

ER -

Gao F, Xue X, Wang J, Sun J, Hussain A, Yang E. Visual attention model with a novel learning strategy and its application to target detection from SAR images. In Liu C-L, Hussain A, Luo B, Tan KC, Zeng Y, Zhang Z, editors, Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings. Cham, Switzerland: Springer-Verlag. 2016. p. 149-160. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-49685-6_14