Improved Simple Linear Iterative Clustering Algorithm Using HSL Color Space

Fan Su; Hui Xu; Guodong Chen; Zhenhua Wang; Lining Sun; Zheng Wang

doi:10.1007/978-3-030-27541-9_34

Improved Simple Linear Iterative Clustering Algorithm Using HSL Color Space

Fan Su
, Hui Xu
, Guodong Chen^*
, Zhenhua Wang
, Lining Sun
, Zheng Wang

^*Corresponding author for this work

Soochow University
Shanghai No. 6 People’s Hospital

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

4 Scopus citations

Abstract

Image processing is a very important technical support in robotic vision. As a preprocessing step for image processing, superpixel segmentation is one of the significant branches of image segmentation. Simple linear iterative clustering (SLIC) algorithm, as a widely used superpixel segmentation algorithm, can help to deal with boundary adherence and reduce computational cost for image segmentation. However, the segmentation result of the original SLIC algorithm fails to adhere well to boundary of object, causing undersegmentation sometimes. Therefore, the work proposed an improved method based on SLIC to address the problem of undersegmentation. First, HSL color space was introduced to have a better recognition and processing of color instead of CIELAB color space. In addition, adding flexible combinations of weight coefficient for HSL can achieve different results. Finally, an edge detection strategy was used to enhance the accuracy of superpixel segmentation. The quantification effect of the proposed method was verified using the dataset BSDS500. The experimental results show that the improved algorithm has better accuracy and efficiency.

Original language	English
Title of host publication	Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings
Editors	Haibin Yu, Jinguo Liu, Lianqing Liu, Yuwang Liu, Zhaojie Ju, Dalin Zhou
Publisher	Springer Verlag
Pages	413-425
Number of pages	13
ISBN (Print)	9783030275402
DOIs	https://doi.org/10.1007/978-3-030-27541-9_34
State	Published - 2019
Externally published	Yes
Event	12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 - Shenyang, China Duration: 8 Aug 2019 → 11 Aug 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11744 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Intelligent Robotics and Applications, ICIRA 2019
Country/Territory	China
City	Shenyang
Period	8/08/19 → 11/08/19

Keywords

HSL color space
Robotic vision
SLIC
Sobel operator
Superpixel

Access to Document

10.1007/978-3-030-27541-9_34

Cite this

Su, F., Xu, H., Chen, G., Wang, Z., Sun, L., & Wang, Z. (2019). Improved Simple Linear Iterative Clustering Algorithm Using HSL Color Space. In H. Yu, J. Liu, L. Liu, Y. Liu, Z. Ju, & D. Zhou (Eds.), Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings (pp. 413-425). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11744 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-27541-9_34

Su, Fan ; Xu, Hui ; Chen, Guodong et al. / Improved Simple Linear Iterative Clustering Algorithm Using HSL Color Space. Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings. editor / Haibin Yu ; Jinguo Liu ; Lianqing Liu ; Yuwang Liu ; Zhaojie Ju ; Dalin Zhou. Springer Verlag, 2019. pp. 413-425 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{7b628ad991a840ccb027789f211c188f,

title = "Improved Simple Linear Iterative Clustering Algorithm Using HSL Color Space",

abstract = "Image processing is a very important technical support in robotic vision. As a preprocessing step for image processing, superpixel segmentation is one of the significant branches of image segmentation. Simple linear iterative clustering (SLIC) algorithm, as a widely used superpixel segmentation algorithm, can help to deal with boundary adherence and reduce computational cost for image segmentation. However, the segmentation result of the original SLIC algorithm fails to adhere well to boundary of object, causing undersegmentation sometimes. Therefore, the work proposed an improved method based on SLIC to address the problem of undersegmentation. First, HSL color space was introduced to have a better recognition and processing of color instead of CIELAB color space. In addition, adding flexible combinations of weight coefficient for HSL can achieve different results. Finally, an edge detection strategy was used to enhance the accuracy of superpixel segmentation. The quantification effect of the proposed method was verified using the dataset BSDS500. The experimental results show that the improved algorithm has better accuracy and efficiency.",

keywords = "HSL color space, Robotic vision, SLIC, Sobel operator, Superpixel",

author = "Fan Su and Hui Xu and Guodong Chen and Zhenhua Wang and Lining Sun and Zheng Wang",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 ; Conference date: 08-08-2019 Through 11-08-2019",

year = "2019",

doi = "10.1007/978-3-030-27541-9\_34",

language = "英语",

isbn = "9783030275402",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "413--425",

editor = "Haibin Yu and Jinguo Liu and Lianqing Liu and Yuwang Liu and Zhaojie Ju and Dalin Zhou",

booktitle = "Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings",

address = "德国",

}

Su, F, Xu, H, Chen, G, Wang, Z, Sun, L & Wang, Z 2019, Improved Simple Linear Iterative Clustering Algorithm Using HSL Color Space. in H Yu, J Liu, L Liu, Y Liu, Z Ju & D Zhou (eds), Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11744 LNAI, Springer Verlag, pp. 413-425, 12th International Conference on Intelligent Robotics and Applications, ICIRA 2019, Shenyang, China, 8/08/19. https://doi.org/10.1007/978-3-030-27541-9_34

Improved Simple Linear Iterative Clustering Algorithm Using HSL Color Space. / Su, Fan; Xu, Hui; Chen, Guodong et al.
Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings. ed. / Haibin Yu; Jinguo Liu; Lianqing Liu; Yuwang Liu; Zhaojie Ju; Dalin Zhou. Springer Verlag, 2019. p. 413-425 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11744 LNAI).

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

TY - GEN

T1 - Improved Simple Linear Iterative Clustering Algorithm Using HSL Color Space

AU - Su, Fan

AU - Xu, Hui

AU - Chen, Guodong

AU - Wang, Zhenhua

AU - Sun, Lining

AU - Wang, Zheng

PY - 2019

Y1 - 2019

N2 - Image processing is a very important technical support in robotic vision. As a preprocessing step for image processing, superpixel segmentation is one of the significant branches of image segmentation. Simple linear iterative clustering (SLIC) algorithm, as a widely used superpixel segmentation algorithm, can help to deal with boundary adherence and reduce computational cost for image segmentation. However, the segmentation result of the original SLIC algorithm fails to adhere well to boundary of object, causing undersegmentation sometimes. Therefore, the work proposed an improved method based on SLIC to address the problem of undersegmentation. First, HSL color space was introduced to have a better recognition and processing of color instead of CIELAB color space. In addition, adding flexible combinations of weight coefficient for HSL can achieve different results. Finally, an edge detection strategy was used to enhance the accuracy of superpixel segmentation. The quantification effect of the proposed method was verified using the dataset BSDS500. The experimental results show that the improved algorithm has better accuracy and efficiency.

AB - Image processing is a very important technical support in robotic vision. As a preprocessing step for image processing, superpixel segmentation is one of the significant branches of image segmentation. Simple linear iterative clustering (SLIC) algorithm, as a widely used superpixel segmentation algorithm, can help to deal with boundary adherence and reduce computational cost for image segmentation. However, the segmentation result of the original SLIC algorithm fails to adhere well to boundary of object, causing undersegmentation sometimes. Therefore, the work proposed an improved method based on SLIC to address the problem of undersegmentation. First, HSL color space was introduced to have a better recognition and processing of color instead of CIELAB color space. In addition, adding flexible combinations of weight coefficient for HSL can achieve different results. Finally, an edge detection strategy was used to enhance the accuracy of superpixel segmentation. The quantification effect of the proposed method was verified using the dataset BSDS500. The experimental results show that the improved algorithm has better accuracy and efficiency.

KW - HSL color space

KW - Robotic vision

KW - SLIC

KW - Sobel operator

KW - Superpixel

UR - https://www.scopus.com/pages/publications/85070804724

U2 - 10.1007/978-3-030-27541-9_34

DO - 10.1007/978-3-030-27541-9_34

M3 - 会议稿件

AN - SCOPUS:85070804724

SN - 9783030275402

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

SP - 413

EP - 425

BT - Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings

A2 - Yu, Haibin

A2 - Liu, Jinguo

A2 - Liu, Lianqing

A2 - Liu, Yuwang

A2 - Ju, Zhaojie

A2 - Zhou, Dalin

PB - Springer Verlag

T2 - 12th International Conference on Intelligent Robotics and Applications, ICIRA 2019

Y2 - 8 August 2019 through 11 August 2019

ER -

Su F, Xu H, Chen G, Wang Z, Sun L, Wang Z. Improved Simple Linear Iterative Clustering Algorithm Using HSL Color Space. In Yu H, Liu J, Liu L, Liu Y, Ju Z, Zhou D, editors, Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings. Springer Verlag. 2019. p. 413-425. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-27541-9_34

Improved Simple Linear Iterative Clustering Algorithm Using HSL Color Space

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