山东大学学报 (工学版)    2021 51 (4): 1-7   ISSN: 1672-3961  CN: 37-1391/T  

图像依赖的显著图融合方法
梁晔1,2(),马楠2,刘宏哲1
1. 北京联合大学信息服务工程重点实验室, 北京 100101
2. 北京联合大学机器人学院, 北京 100044
收稿日期 2020-03-31  修回日期 null  网络版发布日期 2021-08-18
参考文献  
1 KOCH Christof , ULLMAN Shimon . Shifts in selective visual attention: towards the underlying neural circuitry[J]. Human Neurbiology, 1985, 4 (4): 219- 227.
2 ITTI Laurent , KOCH Christof , Niebur Ernst . 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.
3 CHENG Mingming , ZHANG Guoxin , MITRA Niloy J , et al. Global contrast based salient region detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37 (3): 569- 582.
4 SHEN Xiaohui, WU Ying. A unified approach to salient object detection via low rank matrix Recovery[C]//Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. Providence, America: IEEE Press, 2012: 853-860.
5 CHENG Mingming, WARRELL Jonathan, LIN Wenyan, et al. Efficient salient region detection with soft image abstraction[C]//Proceedings of IEEE International Conference on Computer Vision. Sydney, Australia: IEEE Press, 2013: 1529-1536.
6 YAN Qiong, XU Li, SHI Jianping, et al. Hierarchical saliency detection[C]//Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. Portland, America: IEEE Press, 2013: 1155-1162.
7 MARGOLIN Ran, TAL Ayellet, ZELNIKMANOR Lihi. What makes a patch distinct[C]// Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. Portland, America: IEEE Press, 2013: 1139-1146.
8 JUDD Tilke M, EHINGER Krista, DURAND Frédo, et al. Learning to predict where humans look[C]//Proceedings of IEEE International Conference on Computer Vision. Kyoto, Japan: IEEE Press, 2009: 2106-2113.
9 BORJI Ali. Boosting bottom-up and top-down visual features for saliency estimation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Rhode Island, America: IEEE Press, 2012, 157(10): 438-445.
10 CHENG Mingming, LIU Yun, LIN Wenyan, et al. Bing: binarized normed gradients for objectness estimation at 300fps[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, America: IEEE Press, 2014: 3286-3293.
11 WANG Jingdong , JIANG Huaizu , YUAN Zejian , et al. Salient object detection: A discriminative regional feature integration approach[J]. International Journal of Computer Vision, 2017, 123 (2): 251- 268.
12 WEI Yichen, WEN Fang, ZHU Wangjiang, et al. Geodesic saliency using background priors[C]//Proceedings of the European Conference on Computer Vision. Firenze, Italy: Springer, 2012: 29-42.
13 MAI Long, NIU Yuzhen, LIU Feng. Saliency aggregation: a data driven approach[C]//Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. Portland, America: IEEE Press, 2013: 1131-1138.
14 LU Huchuan, RUAN Xiang, YANG Minghsuan. Deep networks for saliency detection via local estimation and global search[C]//Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. Boston, America: IEEE Press, 2015: 3183-3192.
15 LI Guanbin, YU Yizhou. Visual saliency based on multiscale deep features[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Boston, America: IEEE Press, 2015: 5455-5463.
16 ZHAO Rui, OUYANG Wanli, LI Hongsheng, et al. Saliency detection by multi-context deep learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, America: IEEE Press, 2015: 1265-1274.
17 ZHANG Pingping, WANG Dong, LU Huchuan, et al. Amulet: aggregating multi-level convolutional features for salient object detection[C]//Proceedings of IEEE International Conference on Computer Vision. Venice, Italy: IEEE Press, 2017: 202-211.
18 HOU Qibin, CHENG Mingming, HU Xiaowei, et al. Deeply supervised salient object detection with short connection[C]// Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. Hawai, America: IEEE Press, 2017: 3203-3212.
19 HU Xiaowei, ZHU Lei, QIN Jing, et al. Recurrently aggregating deep features for salient object detection[C]//Proceedings of Thirty-Second AAAI Conference on Artificial Intelligence. Louisiana, America: AAAI Press, 2018: 6943-6950.
20 LIU Jiangjiang, HOU Qibin, CHENG Mingming, et al. A simple pooling-based design for real-time salient object detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. California, America: IEEE Press, 2019: 3917-3926.
21 QIN Xuebin, ZHANG Zichen, HUANG Chenyang, et al. BASNet: boundary-aware salient object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Los Angeles, America: IEEE Press, 2019: 7479-7489.
22 MAI Long, LIU Feng. Comparing salient object detection results without ground truth[C]//Proceedings of European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014: 76-91.
23 LI Guanbin, YU Yizhou. Deep contrast learning for salient object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, America: IEEE Press, 2016: 478-487.
24 LEE Gayoung, TAI Yuwing, KIM Junmo. Deep saliency with encoded low level distance map and high level features[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, America: IEEE Press, 2016: 660-668.
25 LI Xi , ZHAO Liming , WEI Lina , et al. Deep saliency: multi-task deep neural network model for salient object detection[J]. IEEE Transactions on Image Processing, 2016, 25 (8): 3919- 3930.
26 JIANG Bowen, ZHANG Lihe, LU Huchuan, et al. Saliency detection via absorbing markov chain[C]//Proceedings of IEEE International Conference on Computer Vision. Sydney, Australia: IEEE Press, 2013: 1665-1672.
27 YANG Chuan, ZHANG Lihe, LU Huchuan, et al. Saliency detection via graph-based manifold ranking[C]//2013 IEEE Conference on Computer Vision and Pattern Recognition. Portlan, America: IEEE Press, 2013: 3166-3173.
28 QIN Yao, LU Huchuan, XU Yiqun. Saliency detection via cellular automata[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Boston, America: IEEE Press, 2015: 110-119.
29 ZHANG Jianming, SCLAROFF Stan, LIN Zhe, et al. Minimum barrier salient object detection at 80 FPS[C]//Proceedings of IEEE International Conference on Computer Vision. Santiago, Chile: IEEE Press, 2015: 1404-1412.
30 MARGOLIN Ran, ZELNIKMANOR Lihi, TAL Ayellet. How to evaluate foreground maps?[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbu, America: IEEE Press, 2014: 248-255.
31 FAN Dengping, CHENG Mingming, LIU Yun. Structure- measure: a new way to evaluate foreground maps[C]//Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy: IEEE Press, 2017: 4548-4557.
32 FAN Dengping, GONG Cheng, CAO Yang, et al. Enhanced-alignment measure for binary foreground map evaluation[C]//Proceedings of the International Joint Conference on Artificial Intelligence. Stockholm, Sweden: IJCAI Press, 2018: 698-704.

通讯作者: