A saliency detection model based on sparse features and visual acuity

Yuming Fang; Weisi Lin; Zhenzhong Chen; Chia Wen Lin; Zhijun Fang; Chenwei Deng

doi:10.1109/ISCAS.2013.6572482

A saliency detection model based on sparse features and visual acuity

Yuming Fang, Weisi Lin, Zhenzhong Chen, Chia Wen Lin, Zhijun Fang, Chenwei Deng

School of Information and Electronics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a novel computational model of visual attention based on the relevant characteristics of the Human Visual System (HVS). The input image is firstly divided into small image patches. Then the sparse features for each image patch are extracted based on the learned sparse coding basis. The human visual acuity is adopted in the calculation of the center-surround feature differences for saliency detection. In addition, the neighboring image patches for computing the saliency value of each center image patch are selected based on the characteristics of HVS. Experimental results show that the proposed saliency detection algorithm outperforms other existing schemes tested with a large public image database.

Original language	English
Title of host publication	2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013
Pages	2888-2891
Number of pages	4
DOIs	https://doi.org/10.1109/ISCAS.2013.6572482
Publication status	Published - 2013
Event	2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013 - Beijing, China Duration: 19 May 2013 → 23 May 2013

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)	0271-4310

Conference

Conference	2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013
Country/Territory	China
City	Beijing
Period	19/05/13 → 23/05/13

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10.1109/ISCAS.2013.6572482

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@inproceedings{407a53ff77524443a5c2356958ba9c41,

title = "A saliency detection model based on sparse features and visual acuity",

abstract = "In this paper, we propose a novel computational model of visual attention based on the relevant characteristics of the Human Visual System (HVS). The input image is firstly divided into small image patches. Then the sparse features for each image patch are extracted based on the learned sparse coding basis. The human visual acuity is adopted in the calculation of the center-surround feature differences for saliency detection. In addition, the neighboring image patches for computing the saliency value of each center image patch are selected based on the characteristics of HVS. Experimental results show that the proposed saliency detection algorithm outperforms other existing schemes tested with a large public image database.",

author = "Yuming Fang and Weisi Lin and Zhenzhong Chen and Lin, {Chia Wen} and Zhijun Fang and Chenwei Deng",

year = "2013",

doi = "10.1109/ISCAS.2013.6572482",

language = "English",

isbn = "9781467357609",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

pages = "2888--2891",

booktitle = "2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013",

note = "2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013 ; Conference date: 19-05-2013 Through 23-05-2013",

}

Fang, Y, Lin, W, Chen, Z, Lin, CW, Fang, Z & Deng, C 2013, A saliency detection model based on sparse features and visual acuity. in 2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013., 6572482, Proceedings - IEEE International Symposium on Circuits and Systems, pp. 2888-2891, 2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013, Beijing, China, 19/05/13. https://doi.org/10.1109/ISCAS.2013.6572482

A saliency detection model based on sparse features and visual acuity. / Fang, Yuming; Lin, Weisi; Chen, Zhenzhong et al.
2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013. 2013. p. 2888-2891 6572482 (Proceedings - IEEE International Symposium on Circuits and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A saliency detection model based on sparse features and visual acuity

AU - Fang, Yuming

AU - Lin, Weisi

AU - Chen, Zhenzhong

AU - Lin, Chia Wen

AU - Fang, Zhijun

AU - Deng, Chenwei

PY - 2013

Y1 - 2013

N2 - In this paper, we propose a novel computational model of visual attention based on the relevant characteristics of the Human Visual System (HVS). The input image is firstly divided into small image patches. Then the sparse features for each image patch are extracted based on the learned sparse coding basis. The human visual acuity is adopted in the calculation of the center-surround feature differences for saliency detection. In addition, the neighboring image patches for computing the saliency value of each center image patch are selected based on the characteristics of HVS. Experimental results show that the proposed saliency detection algorithm outperforms other existing schemes tested with a large public image database.

AB - In this paper, we propose a novel computational model of visual attention based on the relevant characteristics of the Human Visual System (HVS). The input image is firstly divided into small image patches. Then the sparse features for each image patch are extracted based on the learned sparse coding basis. The human visual acuity is adopted in the calculation of the center-surround feature differences for saliency detection. In addition, the neighboring image patches for computing the saliency value of each center image patch are selected based on the characteristics of HVS. Experimental results show that the proposed saliency detection algorithm outperforms other existing schemes tested with a large public image database.

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U2 - 10.1109/ISCAS.2013.6572482

DO - 10.1109/ISCAS.2013.6572482

M3 - Conference contribution

AN - SCOPUS:84883408737

SN - 9781467357609

T3 - Proceedings - IEEE International Symposium on Circuits and Systems

SP - 2888

EP - 2891

BT - 2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013

T2 - 2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013

Y2 - 19 May 2013 through 23 May 2013

ER -

A saliency detection model based on sparse features and visual acuity

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