[1]赵闰霞,蹇木伟,齐强,等.基于Object Proposals并集的显著性检测模型[J].智能系统学报,2018,13(6):946-951.[doi:10.11992/tis.201801009]
ZHAO Runxia,JIAN Muwei,QI Qiang,et al.Saliency detection model based on the union of Object Proposals[J].CAAI Transactions on Intelligent Systems,2018,13(6):946-951.[doi:10.11992/tis.201801009]
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ZHAO Runxia,JIAN Muwei,QI Qiang,et al.Saliency detection model based on the union of Object Proposals[J].CAAI Transactions on Intelligent Systems,2018,13(6):946-951.[doi:10.11992/tis.201801009]
基于Object Proposals并集的显著性检测模型
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第6期
页码:
946-951
栏目:
学术论文—机器学习
出版日期:
2018-10-25
- Title:
- Saliency detection model based on the union of Object Proposals
- Keywords:
- saliency detection; Object Proposal; superpixels; texture; background map; global contrast; boundary connectivity; bottom-up
- 分类号:
- TP391
- 摘要:
- 针对当前常见的显著性检测模型得到的结果会包含大量的背景区域的缺点，本文提出了基于Object Proposals并集的显著性检测模型。该模型首先对于输入图片生成一系列Object Proposals，并通过其并集计算得到背景图；然后结合纹理特征和全局对比度得到初始显著图；最后，用得到的背景图对初始显著图进行背景抑制得到最终显著图。实验结果表明，在通用MSRA1000数据集上，本文提出的显著性模型与其他5种方法相比取得了很好的效果。
- Abstract:
- In saliency detection, current existing models usually produce results containing many background regions. To improve the performance, a novel saliency detection model is proposed based on the union of object proposals. The model first generates a series of object proposals from the input pictures, and then gets the background map by computing the union, and then obtains the initial saliency map by combining the texture and global contrast. Finally, the final saliency map is derived by restraining the initial saliency map with the obtained background map. Experimental results on the general MSRA1000 dataset demonstrate that the proposed saliency model performs well compared to the other five existing methods.
- 参考文献/References:
-
[1] 王超, 王浩, 王伟, 等. 基于优化ROI的医学图像分割与压缩方法研究[J]. 重庆邮电大学学报:自然科学版, 2015, 27(2):279-284 WANG Chao, WANG Hao, WANG Wei, et al. Study of optimized ROI based medical image segmentation and compression method[J]. Journal of Chongqing university of posts and telecommunications:natural science edition, 2015, 27(2):279-284
[2] 杨林娜, 安玮, 林再平, 等. 基于空间距离改进的视觉显著性弱小目标检测[J]. 光学学报, 2015, 35(7):0715004 YANG Linna, AN Wei, LIN Zaiping, et al. Small target detection based on visual saliency improved by spatial distance[J]. Acta optica sinica, 2015, 35(7):0715004
[3] ITTI L. Automatic foveation for video compression using a neurobiological model of visual attention[J]. IEEE transactions on image processing, 2004, 13(10):1304-1318.
[4] CHEN Tao, CHENG Mingming, TAN Ping, et al. Sketch2photo:internet image montage[J]. ACM transactions on graphics, 2009, 28(5):1-10.
[5] CHENG Mingming, MITRA N J, HUANG Xiaolei, et al. Global contrast based salient region detection[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(3):569-582.
[6] HAREL J, KOCH C, PERONA P. Graph-based visual saliency[C]//Proceedings of the 19th International Conference on Neural Information Processing Systems. Cambridge, MA, USA, 2007:545-552.
[7] ERDEM E, ERDEM A. Visual saliency estimation by nonlinearly integrating features using region covariances[J]. Journal of vision, 2013, 13(4):11.
[8] MURRAY N, VANRELL M, OTAZU X, et al. Saliency estimation using a non-parametric low-level vision model[C]//Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC, USA, 2011:433-440.
[9] SHI Jianping, YAN Qiong, XU Li, et al. Hierarchical image saliency detection on extended CSSD[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 38(4):717-729.
[10] YANG Chuan, ZHANG Lihe, LU Huchuan, et al. Saliency detection via graph-based manifold ranking[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Washington, DC, USA, 2013:3166-3173.
[11] LIU Tie, YUAN Zejian, SUN Jian, et al. Learning to detect a salient object[J]. IEEE transactions on pattern analysis and machine intelligence, 2011, 33(2):353-367.
[12] YANG Jimei, YANG M H. Top-down visual saliency via joint CRF and dictionary learning[C]//Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR). Washington, DC, USA, 2012:2296-2303.
[13] ALEXE B, DESELAERS T, FERRARI V. Measuring the objectness of image windows[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 34(11):2189-2202.
[14] CHENG Mingming, ZHANG Ziming, LIN Wenyan, et al. BING:Binarized normed gradients for objectness estimation at 300fps[C]//Proceedings of IEEE conference on computer vision and pattern recognition. Columbus, OH, USA, 2014:3286-3293.
[15] ZITNICK C L, DOLLáR P. Edge boxes:Locating object proposals from edges[C]//Proceedings of European Conference on Computer Vision. Cham:Springer, 2014:391-405.
[16] ACHANTA R, SHAJI A, 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):2274-2282.
[17] HUANG Fang, QI Jinqing, LU Huchuan, et al. Salient object detection via multiple instance learning[J]. IEEE transactions on image processing, 2017, 26(4):1911-1922.
[18] PERAZZI F, KR?HENBüHL P, PRITCH Y, et al. Saliency filters:Contrast based filtering for salient region detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA, 2012:733-740.
[19] XI Tao, ZHAO Wei, WANG Han, et al. Salient object detection with spatiotemporal background priors for video[J]. IEEE transactions on image processing, 2017, 26(7):3425-3436.
[20] ZHU Wangjiang, LIANG Shuang, WEI Yichen, et al. Saliency optimization from robust background detection[C]//Proceedings of IEEE conference on computer vision and pattern recognition. Columbus, OH, USA, 2014:2814-2821.
[21] 刘丽, 匡纲要. 图像纹理特征提取方法综述[J]. 中国图象图形学报, 2009, 14(4):622-635 LIU li, KUANG Gangyao. Overview of image textural feature extraction methods[J]. Journal of image and graphics, 2009, 14(4):622-635
[22] 王佐成, 薛丽霞. 一种新的纹理基元发现及表达方法[J]. 重庆邮电大学学报:自然科学版, 2011, 23(1):115-120 WANG Zuocheng, XUE Lixia. A new representation method of image texton[J]. Journal of Chongqing university of posts and telecommunications:natural science edition, 2011, 23(1):115-120
[23] WANG Qi, YUAN Yuan, YAN Pingkun. Visual saliency by selective contrast[J]. IEEE transactions on circuits and systems for video technology, 2013, 23(7):1150-1155.
[24] HARTIGAN J A, WONG M A. Algorithm as 136:a k-means clustering algorithm[J]. Journal of the royal statistical society. series c (applied statistics), 1979, 28(1):100-108.
[25] HOU Xiaodi, ZHANG Liqing. Saliency detection:a spectral residual approach[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, MN, USA, 2007:1-8.
[26] ACHANTA R, HEMAMI S, Estrada F, et al. Frequency-tuned salient region detection[C]//Proceedings of Conference on Computer vision and pattern recognition. Miami, FL, USA, 2009:1597-1604.
[27] ZHAI Yun, SHAH M. Visual attention detection in video sequences using spatiotemporal cues[C]//Proceedings of the 14th ACM international conference on Multimedia. New York, NY, USA, 2006:815-824.
[28] WEI Y, WEN F, ZHU W, et al. Geodesic saliency using background priors[J]. Computer vision-ECCV 2012, 2012:29-42.
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备注/Memo
收稿日期:2018-01-08。
基金项目:国家自然科学基金项目（61601427，61602229）.
作者简介:赵闰霞,女,1993年生,硕士研究生,主要研究方向为图像处理;蹇木伟,男,1982年生,教授,博士生导师,CCF计算机视觉专委会委员,CCF多媒体专委会委员,CCF机器学习与模式识别通讯委员,山东数媒专委会委员等。主要研究方向为图像处理、模式识别、多媒体计算、机器学习、认知科学。主持国家自然科学基金等研究课题10余项。以第一发明人或第一申请人被授予3项国家专利,其中1项国家发明专利和2项国家实用新型专利。发表学术论文50余篇。被SCI检索的国际期刊论文14篇、被EI检索论文40余篇;齐强,男,1990年生,硕士研究生,主要研究方向为图像处理、模式识别、水下视觉。
通讯作者:蹇木伟.E-mail:20173016@sdufe.edu.cn
更新日期/Last Update:
2018-12-25