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基于优化的ELM和深度层次的RGB-D显著检测

刘政怡 徐天泽

引用本文: 刘政怡, 徐天泽. 基于优化的ELM和深度层次的RGB-D显著检测[J]. 电子与信息学报, doi: 10.11999/JEIT180826 shu
Citation:  Zhengyi LIU, Tianze XU. RGB-D Saliency Detection Based on Optimized ELM and Depth Level[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT180826 shu

基于优化的ELM和深度层次的RGB-D显著检测

    作者简介: 刘政怡: 女,1978年生,副教授,研究方向为计算机视觉;
    徐天泽: 男,1994年生,硕士生,研究方向为计算机视觉;
    通讯作者: 刘政怡, 22927463@qq.com
  • 基金项目: 安徽省自然科学基金(1908085MF182)

摘要: 目前,相当多的显著目标检测方法均聚焦于2D的图像上,而RGB-D图像所需要的显著检测方法与单纯的2D图像相去甚远,这就需要新的适用于RGB-D的显著检测方法。该文在经典的RGB显著检测方法,即极限学习机的应用的基础上,提出融合了特征提取、前景增强、深度层次检测等多种思路的新的RGB-D显著性检测方法。该文的方法是:第一,运用特征提取的方法,提取RGB图4个超像素尺度的4096维特征;第二,依据特征提取中产生的4个尺度的超像素数量,分别提取RGB图的RGB, LAB, LBP特征以及深度图的LBE特征;第三,根据LBE和暗通道特征两种特征求出粗显著图,并在4个尺度的遍历中不断强化前景、削弱背景;第四,根据粗显著图选取前景与背景种子,放入极限学习机中进行分类,得到第1阶段显著图;第五,运用深度层次检测、图割等方法对第1阶段显著图进行再次优化,得到第2阶段显著图,即最终显著图。

English

    1. [1]

      GOFERMAN S, ZELNIK-MANOR L, and TAL A. Context-aware saliency detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(10): 1915–1926. doi: 10.1109/TPAMI.2011.272

    2. [2]

      ROTHER C, KOLMOGOROV V, and BLAKE A. "GrabCut": interactive foreground extraction using iterated graph cuts[J]. ACM Transactions on Graphics, 2004, 23(3): 309–314. doi: 10.1145/1015706.1015720

    3. [3]

      DING Yuanyuan, XIAO Jing, and YU Jingyi. Importance filtering for image retargeting[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA, 2011: 89–96.

    4. [4]

      MAHADEVAN V and VASCONCELOS N. Saliency-based discriminant tracking[C]. Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 2009: 1007–1013.

    5. [5]

      SIAGIAN C and ITTI L. Rapid biologically-inspired scene classification using features shared with visual attention[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2): 300–312. doi: 10.1109/TPAMI.2007.40

    6. [6]

      YANG Chuan, ZHANG Lihe, LU Huchuan, et al. Saliency detection via graph-based manifold ranking[C]. Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013: 3166–3173.

    7. [7]

      TONG Na, LU Huchuan, RUAN Xiang, et al. Salient object detection via bootstrap learning[C]. Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 2015: 1884–1892.

    8. [8]

      PERAZZI F, KRÄHENBÜHL P, PRITCH Y, et al. Saliency filters: Contrast based filtering for salient region detection[C]. Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012: 733–740.

    9. [9]

      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. doi: 10.1109/TPAMI.2014.2345401

    10. [10]

      KLEIN D A and FRINTROP S. Center-surround divergence of feature statistics for salient object detection[C]. Proceedings of 2011 IEEE International Conference on Computer Vision, Barcelona, Spain, 2011: 2214–2219.

    11. [11]

      PENG Houwei, LI Bing, XIONG Weihua, et al. RGBD salient object detection: A benchmark and algorithms[M]. FLEET D, PAJDLA T, SCHIELE B, et al. Computer Vision - ECCV 2014. Cham: Springer, 2014: 92–109.

    12. [12]

      ZHANG Pingping, WANG Dong, LU Huchuan, et al. Learning uncertain convolutional features for accurate saliency detection[C]. Proceedings of 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 212–221.

    13. [13]

      XUE Haoyang, GU Yun, LI Yijun, et al. RGB-D saliency detection via mutual guided manifold ranking[C]. Proceedings of 2015 IEEE International Conference on Image Processing, Quebec City, QC, Canada, 2015: 666–670.

    14. [14]

      ZHANG Lu, LIU Jianhua, and LU Huchuan. Saliency detection via extreme learning machine[J]. Neurocomputing, 2016, 218: 103–112. doi: 10.1016/j.neucom.2016.08.066

    15. [15]

      LI Guanbin and YU Yizhou. Visual saliency based on multiscale deep features[C]. Proceedings of 2015 IEEE Computer Vision and Pattern Recognition, Boston, MA, USA, 2015: 5455–5463.

    16. [16]

      FENG D, BARNES N, YOU Shaodi, et al. Local background enclosure for RGB-D salient object detection[C]. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016: 2343–2350.

    17. [17]

      KRIZHEVSKY A, SUTSKEVER I, and 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, 2012: 1097–1105.

    18. [18]

      GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014: 580–587.

    19. [19]

      DONAHUE J, JIA Yangqing, VINYALS O, et al. DeCAF: A deep convolutional activation feature for generic visual recognition[C]. Proceedings of the 31st International Conference on International Conference on Machine Learning, Beijing, China, 2014: 647–655.

    20. [20]

      RAZAVIAN A S, AZIZPOUR H, SULLIVAN J, et al. CNN features off-the-shelf: An astounding baseline for recognition[C]. Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA, 2014: 806–813.

    21. [21]

      GUO Jingfang, REN Tongwei, HUANG Lei, et al. Saliency detection on sampled images for tag ranking[J]. Multimedia Systems, 2019, 25(1): 35–47. doi: 10.1007/s00530-017-0546-9

    22. [22]

      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

    23. [23]

      HUANG Guangbin. What are extreme learning machines? Filling the gap between frank Rosenblatt’s dream and john von Neumann’s puzzle[J]. Cognitive Computation, 2015, 7(3): 263–278. doi: 10.1007/s12559-015-9333-0

    24. [24]

      CAO Weipeng, MING Zhong, WANG Xizhao, et al. Improved bidirectional extreme learning machine based on enhanced random search[J]. Memetic Computing, 2019, 11(1): 19–26. doi: 10.1007/s12293-017-0238-1

    25. [25]

      ESHTAY M, FARIS H, and OBEID N. Improving extreme learning machine by competitive swarm optimization and its application for medical diagnosis problems[J]. Expert Systems with Applications, 2018, 104: 134–152. doi: 10.1016/j.eswa.2018.03.024

    26. [26]

      BOYKOV Y, VEKSLER O, and ZABIH R. Fast approximate energy minimization via graph cuts[C]. Proceedings of the 7th IEEE International Conference on Computer Vision, Kerkyra, Greece, 1999: 377–384.

    27. [27]

      LIU Shuang, ZHANG Zhong, XIAO Baihua, et al. Ground-based cloud detection using automatic graph cut[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(6): 1342–1346. doi: 10.1109/LGRS.2015.2399857

    28. [28]

      YU Kai, CHEN Xinjian, SHI Fei, et al. A novel 3D graph cut based co-segmentation of lung tumor on PET-CT images with Gaussian mixture models[C]. Proceedings of SPIE 9784, Medical Imaging 2016: Image Processing, San Diego, California, United States, 2016: 97842V.

    29. [29]

      JU Ran, GE Ling, GENG Wenjing, et al. Depth saliency based on anisotropic center-surround difference[C]. Proceedings of 2014 IEEE International Conference on Image Processing, Paris, France, 2014: 1115–1119.

    30. [30]

      CHENG Yupeng, FU Huazhu, WEI Xingxing, et al. Depth enhanced saliency detection method[C]. Proceedings of International Conference on Internet Multimedia Computing and Service, Xiamen, China, 2014.

    31. [31]

      REN Jianqiang, GONG Xiaojin, YU Lu, et al. Exploiting global priors for RGB-D saliency detection[C]. Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA, 2015: 25–32.

    32. [32]

      XUE Haoyang, GU Yun, LI Yijun, et al. RGB-D saliency detection via mutual guided manifold ranking[C]. Proceedings of 2015 IEEE International Conference on Image Processing, Quebec City, QC, Canada, 2015: 666–670. (本条文献与第13条重复, 请核对)

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  • 图 1  本算法程序流程图

    图 2  LBE部分显著图与本文对应显著图对比

    图 3  有无流程优化的P-R曲线对比

    图 4  部分图片不同方法显著图像对比

    图 5  各种方法的P-R曲线对比

    表 1  各算法F-measure值

    算法本文ELMACSDMGMRGPLBEDES
    F0.75260.58570.64640.72200.74080.2728
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文章相关
  • 通讯作者:  刘政怡, 22927463@qq.com
  • 收稿日期:  2018-08-22
  • 录用日期:  2019-05-15
  • 网络出版日期:  2019-06-03
通讯作者: 陈斌, bchen63@163.com
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