[1]梁晔,于剑.面向社群图像的显著区域检测方法[J].智能系统学报,2018,13(2):174-181.[doi:10.11992/tis.201706043]
LIANG Ye,YU Jian.Salient region detection for social images[J].CAAI Transactions on Intelligent Systems,2018,13(2):174-181.[doi:10.11992/tis.201706043]
点击复制
LIANG Ye,YU Jian.Salient region detection for social images[J].CAAI Transactions on Intelligent Systems,2018,13(2):174-181.[doi:10.11992/tis.201706043]
面向社群图像的显著区域检测方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第2期
页码:
174-181
栏目:
学术论文—机器感知与模式识别
出版日期:
2018-04-15
- Title:
- Salient region detection for social images
- Keywords:
- saliency; salient region; social images; deep learning; tag
- 分类号:
- TP311
- 摘要:
- 网络技术和社交网站的发展带来了社群图像的飞速增长。海量的社群图像成为了非常重要的图像类型。本文关注社群图像的显著区域检测问题，提出基于深度特征的显著区域检测方法。针对社群图像带有标签的特点，在系统框架中，本文采取两条提取线：基于CNN特征的显著性计算和基于标签的语义计算，二者的结果进行融合。最后，通过全连接的条件随机场模型对融合的显著图进行空间一致性优化。此外，为了验证面向社群图像的显著区域检测方法的性能，针对目前没有面向社群图像的带有标签信息的显著性数据集，基于NUS-WIDE数据集，本文构建了一个图像结构丰富的社群图像数据集。大量的实验证明了所提方法的有效性。
- Abstract:
- The development of network technology and social website has brought about the rapid growth of social images. Massive social images have become a very important image type. This paper focuses on the detection problem of salient region for social images, a method for detecting salient region and based on depth features was proposed. By considering the feature that the social image is attached with tag, in the framework of the system, the paper used two extraction lines: the saliency computing based on CNN features and the semantic computing based on tag, the results of both parts were fused. Finally, saliency maps were optimized by a fully connected conditional random field model for the spatial consistency. In addition, for verifying the performances of the saliency region detection method orienting social image, in view of the lack of saliency dataset with tags for social images, on basis of NUS-WIDE dataset, the paper constructed a social image dataset with rich image structures. Extensive experiments demonstrated the effectiveness of the proposed method.
- 参考文献/References:
-
[1] XIAO Chuanmin, SHI Zelin, XIA Renbo, et al. Edge-detection algorithm based on visual saliency[J]. Information and control, 2014, 43 (1): 9-13.
[2] YAN Qiong, XU Li, SHI Jianping, et al. Hierarchical saliency detection[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Portland, OR, USA, 2013: 1155-1162.
[3] JIANG Huaizu, WANG Jingdong, YUAN Zejian, et al. Salient object detection: a discriminative regional feature integration approach[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Portland, OR, USA, 2013: 2083-2090.
[4] LIN Yuetan, KONG Shu, WANG Donghui, et al. Saliency detection within a deep convolutional architecture[C]//Workshops at the 28th AAAI Conference on Artificial Intelligence. Québec City, Canada, 2014.
[5] LI Guanbin, YU Yizhou. Visual saliency based on multiscale deep features[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA, 2015: 5455-5463.
[6] WANG Lijun, LU Huchuan, RUAN Xiang, et al. Deep networks for saliency detection via local estimation and global search[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA, 2015: 3183-3192.
[7] ZHAO Rui, OUYANG Wanli, LI Hongsheng, et al. Saliency detection by multi-context deep learning[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA, 2015: 1265-1274.
[8] WANG Wen, LANG Congyan, FENG Songhe. Contextualizing tag ranking and saliency detection for social images[M]//LI Shipeng, EL SADDIK A, WANG MENG, et al. Advances in Multimedia Modeling. Berlin Heidelberg, Germany: Springer, 2013: 428-435.
[9] ZHU Guokang, WANG Qi, YUAN Yuan. Tag-saliency: combining bottom-up and top-down information for saliency detection[J]. Computer vision and image understanding, 2014, 118: 40-49.
[10] LIU Tie, SUN Jian, ZHENG Nanning, et al. Learning to detect a salient object[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Minneapolis, Minnesota, USA, 2007: 1-8.
[11] LI Jian, LEVINE M D, AN Xiangjing, et al. Visual saliency based on scale-space analysis in the frequency domain[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(4): 996-1010.
[12] MARTIN D, FOWLKES C, TAL D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C]//Proceedings of the 8th IEEE International Conference on Computer Vision. Vancouver, BC, USA, 2001, 2: 416-423.
[13] BATRA D, KOWDLE A, PARIKH D, et al. iCoseg: Interactive co-segmentation with intelligent scribble guidance[C]//Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). San Francisco, CA, USA, 2010: 3169-3176.
[14] ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency-tuned salient region detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Miami, FL, USA, 2009: 1597-1604.
[15] JIA Yangqing, DEVELOPER L, SHELHAMER E. Caffe [N/OL]. [2016-11-12]. http://caffe.berkeleyvision.org/, 2013.
[16] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, USA, 2012: 1097-1105.
[17] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus, OH, USA, 2014: 580-587.
[18] TONG Na, LU Huchuan, RUAN Xiang, et al. Salient object detection via bootstrap learning[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA, 2015: 1884-1892.
[19] WANG Linzhao, WANG Lijun, LU Huchuan, et al. Saliency detection with recurrent fully convolutional networks[C]//Proceedings of the 14th European Conference Computer Vision-ECCV 2016. Cham, Germany, 2016: 151-157.
[20] KR?HENBüHL P, KOLTUN V. Efficient inference in fully connected CRFs with Gaussian edge potentials[C]//Proceedings of the 24th International Conference on Neural Information Processing Systems. Granada, Spain, 2012: 109-117.
[21] JIANG Huaizu, WANG Jingdong, YUAN Zejian, et al. Automatic salient object segmentation based on context and shape prior[C]//Proceedings of the British Machine Vision Conference (BMVC). Dundee, Scotland, 2011: 1-12.
[22] RAHTU E, KANNALA J, SALO M, et al. Segmenting salient objects from images and videos[C]//Proceedings of the 11th European Conference on Computer Vision (ECCV). Crete, Greece, 2010: 366-379.
[23] CHENG Mingming, ZHANG Guoxin, MITRA N J, et al. Global contrast based salient region detection[C]//Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA, 2011: 409-416.
[24] CHANG K Y, LIU T L, CHEN H T, et al. Fusing generic objectness and visual saliency for salient object detection[C]//Proceedings of the 2011 IEEE International Conference on Computer Vision (ICCV). Barcelona, Spain, 2011: 914-921.
[25] SHEN Xiaohui, WU Ying. A unified approach to salient object detection via low rank matrix Recovery[C]//Proceedings of the 2012 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). Providence, Rhode Island, USA, 2011: 853-860.
[26] PERAZZI F, KR?HENBUHL P, PRITCH Y, et al. Saliency filters: contrast based filtering for salient region detection[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence, Rhode Island, USA, 2012: 733-740.
[27] WEI Yichen, WEN Fang, ZHU Wangjiang, et al. Geodesic saliency using background priors[C]//Proceedings of the 12th European Conference on Computer Vision (ECCV). Florence, Italy, 2012: 29-42.
[28] GOFERMAN S, ZELNIK-MANOR L, TAL A. Context-aware saliency detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). San Francisco, CA, USA, 2010: 2376-2383.
[29] HOU Xiaodi, HAREL J, KOCH C. Image signature: highlighting sparse salient regions[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 34(1): 194-201.
[30] SCHARFENBERGER C, WONG A, FERGANI K, et al. Statistical textural distinctiveness for salient region detection in natural images[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Portland, Oregon, USA, 2013: 979-986.
[31] YANG Chuan, ZHANG Lihe, LU Huchuan, et al. Saliency detection via graph-based manifold ranking[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Portland, OR, USA, 2013: 3166-3173.
[32] MARGOLIN R, TAL A, ZELNIK-MANOR L. What makes a patch distinct?[C]//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Portland, OR, USA, 2013: 1139-1146.
[33] LI Xi, LI Yao, SHEN Chunhua, et al. Contextual hypergraph modeling for salient object detection[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV). Sydney, NSW, Australia, 2013: 3328-3335.
[34] CHENG Mingming, WARRELL J, LIN Wenyan, et al. Efficient salient region detection with soft image abstraction[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV). Sydney, Australia, 2013: 1529-1536.
[35] JIANG Bowen, ZHANG Lihe, LU Huchuan, et al. Saliency detection via absorbing Markov chain[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV). Sydney, NSW, Australia, 2013: 1665-1672.
[36] LI Xiaohui, LU Huchuan, ZHANG Lihe, et al. Saliency detection via dense and sparse reconstruction[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision. Washington, DC, USA, 2013: 2976-2983.
[37] PENG H, LI B, LING H, et al. Salient object detection via structured matrix decomposition[J]. IEEE transactions on pattern analysis & machine intelligence, 2017, 39(4): 818-832.
[38] ZHU Wangjiang, LIANG Shuang, WEI Yichen, et al. Saliency optimization from robust background detection[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus, OH, USA, 2014: 2814-2821.
- 相似文献/References:
-
[1]费宇杰,吴小俊.一种局部聚合描述符和组显著编码相结合的编码方法[J].智能系统学报,2017,12(2):172.[doi:10.11992/tis.201602010]
FEI Yujie,WU Xiaojun.A new feature coding algorithm based on the combination of group salient coding and VLAD[J].CAAI Transactions on Intelligent Systems,2017,12():172.[doi:10.11992/tis.201602010]
备注/Memo
收稿日期:2017-06-11。
基金项目:北京市自然科学基金项目（4182022）；北京联合大学2017年度人才强校百杰计划项目（BPHR2017CZ10）；“十三五”时期北京市属高校高水平教师队伍建设支持计划项目（IDHT20170511）；国家科技支撑计划项目（2015BAH55F03）
作者简介:梁晔,1978年生,女,讲师,主要研究方向为图像处理和机器学习,发表中文核心和三大检索论文10余篇;于剑,男,1969年生,教授,博士生导师,博士,主要研究方向为2005年分别获得第八届北京青年优秀科技论文奖一等奖、第七届詹天佑铁道科技奖北京交通大学专项基金奖,2006年获得霍英东青年教师基金。发表学术论文30余篇。
通讯作者:梁晔.E-mail:liangye@buu.edu.cn.
更新日期/Last Update:
1900-01-01