自适应阈值分割与局部背景线索结合的显著性检测

doi:10.11999/JEIT160984

Abstract
Figure/Table
References (16)
Related Citation (10)

Download: PDF (4470 KB)
Export: BibTeX | EndNote (RIS)

Abstract

In order to improve the applicability for different types of image and integrity of the results, a saliency detection algorithm is proposed. It combines the adaptive threshold merging with a new background selection strategy. In the segmentation process, the color difference sequence is obtained by the selective fusion of RGB and LAB of adjacent blocks. Adaptive threshold is generated by inverse proportion model of block area parameter. Merging progress is done after the adaptive threshold comparison with the color difference sequence. In the background selection process, background regions are obtained by the local relative position of background-subject-background in the local area. The experimental results are optimized for edge. Compared with other algorithms, the saliency map of two values obtained does not need external threshold algorithm in this paper. Adaptive threshold merging can eliminate the details of objects in complex environments and can focus on the saliency comparison of the same level size objects.

Key words： Saliency detection Adaptive threshold Adjacent color difference Local background clues Edge optimization

Received: 29 September 2016 Published: 14 April 2017

PACS:

TP391.41

Fund:

Tianjin Science and Technology Project (14RCGFGX00846, 15ZCZDNC00130), Project of Natural Science Foundation of Hebei Province (F2015202239)

Corresponding Authors: TANG Hongmei E-mail: hmtang2005@163.com

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	TANG Hongmei
	WU Shijing
	GUO Yingchun
	PEI Yanan

Cite this article:

TANG Hongmei,WU Shijing,GUO Yingchun等. Saliency Detection Based on Adaptive Threshold Segmentation and Local Background Clues[J]. JEIT, 2017, 39(7): 1592-1598.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT160984 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I7/1592

[1]	ITTI L, KOCH C, and NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.
[2]	罗会兰, 万成涛, 孔繁胜. 基于 KL散度及多尺度融合的显著性区域检测算法[J]. 电子与信息学报, 2016, 38(7): 1594-1601. doi: 10.11999/JEIT151145.
	LUO Huilan, WAN Chengtao, and KONG Fansheng. Salient region detection algorithm via KL divergence and multi-scale merging[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1594-1601. doi: 10.11999/JEIT151145.
[3]	WU Pohung, CHEN Chienchi, DING Jianjiun, et al. Salient region detection improved by principle component analysis and boundary information[J]. IEEE Transactions on Image Processing, 2013, 22(9): 3614-3624. doi: 10.1109/TIP.2013. 2266099.
[4]	SHETH C and VENKATESH R. Object saliency using a background prior[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai, China, 2016: 1931-1935. doi: 10.1109/ICASSP.2016.7472013.
[5]	LI G B and YU Y Z. Visual saliency based on multiscale deep features[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 2015: 5455-5463. doi: 10.1109/CVPR.2015.7299184.
[6]	ZHANG Wei, BORJI A, WANG Zhou, et al. The application of visual saliency models in objective image quality assessment: A statistical evaluation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(6): 1266-1278. doi: 10.1109/TNNLS.2015.2461603.
[7]	毕笃彦, 库涛, 查宇飞, 等. 基于颜色属性直方图的尺度目标跟踪算法研究[J]. 电子与信息学报, 2016, 38(5): 1099-1106. doi: 10.11999/JEIT150921.
	BI Duyan, KU Tao, ZHA Yufei, et al. Scale-adaptive object tracking based on color names histogram[J].Journal of Electronics & Information Technology, 2016, 38(5): 1099-1106. doi: 10.11999/JEIT150921.
[8]	XIANG D and ZHONG B J. Scale-space saliency detection in combined color space[C]. Chinese Automation Congress, Wuhan, China, 2015: 726-731. doi: 10.1109/CAC.2015. 7382593.
[9]	ACHANTA R, APPU S, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2281. doi: 10.1109/TPAMI.2012.120.
[10]	TONG Na, LU Huchuan, ZHANG Lihe, et al. Saliency detection with multi-scale superpixels[J]. IEEE Signal Processing Letters, 2014, 21(9): 1035-1039. doi: 10.1109/LSP. 2014.2323407.
[11]	郑瑞连, 钟宝江, 徐东升. 基于L曲率的尺度空间形状分析技术[J]. 南京大学学报, 2012, 48(2): 172-181. doi: 10.13232 /j.cnki.jnju.2012.02.007.
	ZHENG Ruilian, ZHONG Baojiang, and XU Dongsheng. Scale space shape analysis technique based on L curvature[J]. Journal of Nanjing University, 2012, 48(2): 172-181. doi: 10.13232/j.cnki.jnju.2012.02.007.
[12]	NGUYEN H T, WORRING M, and BOOMGAARD R V D. Watersnakes: Energy-driven watershed segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(3): 330-342. doi: 10.1109/TPAMI.2003. 1182096.
[13]	朱玉琨. 结合局部特征与空间关系的多物体检测算法研究[D]. [硕士论文], 上海交通大学, 2014: 3-76.
	ZHU Yukun. Research on multi object detection algorithm based on local feature and spatial relation[D]. [Master dissertation], Shanghai Jiao Tong University, 2014: 3-76.
[14]	CHENG M M, ZHANG G X, MITRAN J, et al. Global contrast based salient region detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA, 2011: 409-416. doi: 10.1109/TPAMI.2014. 2345401.
[15]	ZELNIK-MANOR L, TAL A, and MARGOLIN R. What makes a patch distinct?[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013: 1139-1146. doi: 10.1109/CVPR.2013.151.
[16]	CHENG M M, WARRELL L, LIN W Y, et al. Efficient salient region detection with soft image abstraction[C]. IEEE International Conference on Computer Vision, Sydney, Australia, 2013: 1529-1536. doi: 10.1109/ICCV.2013.193.