深度先验图像特征在城市遥感大数据中的应用
作者:
基金项目:

国家重点研发计划(2016YFE0100300)


Application of Image Feature Extraction Based on Depth Prior in Urban Remote Sensing Big Data
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [29]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    图像特征提取始终是计算机视觉和图像处理的核心任务.随着深度学习的快速发展,卷积神经网络逐渐取代传统图像特征算子,成为特征提取的主要算法.本文针对城市遥感数据众包标记系统中的数据关联问题,结合卷积神经网络和池化编码,提出基于深度先验的图像特征提取方法.该特征能有效聚焦室外图像近处物体,并通过图像检索实验验证了其对室外图像的良好表征能力.

    Abstract:

    Image feature extraction is always the core task of computer vision and image processing. With the rapid development of deep learning, the Convolutional Neural Network (CNN) has gradually replaced the traditional image feature operator and became the main algorithm for feature extraction. Combined with CNN and sum pooling, we propose a new image feature extraction algorithm based on depth prior aiming at the data association problem in the crowd sourcing labeling system for urban remote sensing data. The feature can effectively focus on the objects in the vicinity of outdoor images and verify their good characterization of outdoor images via image retrieval experiments.

    参考文献
    1 宋维静, 刘鹏, 王力哲, 等. 遥感大数据的智能处理:现状与挑战. 工程研究-跨学科视野中的工程, 2014, (3):259-265.
    2 Turner BL, Skole D, Sanderson S, et al. Land-Use and land-cover change:science/research plan[J]. Global Change Report, 1995, 43(1995):669-679.
    3 Chi M, Sun Z, Qin Y, et al. A Novel Methodology to Label Urban Remote Sensing Images Based on Location-Based Social Media Photos. Proceedings of the IEEE. 2017. 1926-1936.[doi:10.1109/JPROC.2017.2730585]
    4 Lowe DG. Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE. 1999. 1150-1157.[doi:10.1109/ICCV.1999.790410]
    5 Bay H, Tuytelaars T, Van Gool L. Surf:Speeded up robust features. Computer Vision-ECCV 2006. Gray, Austria. 2006. 404-417.
    6 Dalal N, Triggs B. Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). San Diego, CA, USA. 2005. 886-893.[doi:10.1109/CVPR.2005.177]
    7 Jegou H, Perronnin F, Douze M, et al. Aggregating Local Image Descriptors into Compact Codes. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012, 34(9):1704-1716.[doi:10.1109/TPAMI.2011.235]
    8 Perronnin F, Dance C. Fisher kernels on visual vocabularies for image categorization. 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, MN, USA. 2007. 1-8.[doi:10.1109/CVPR.2007.383266]
    9 Arandjelovic R, Zisserman A. All About VLAD. computer vision and pattern recognition, 2013:1578-1585.
    10 Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Communications of the Acm, 2013, 60(2):2012.
    11 Razavian A S, Azizpour H, Sullivan J, et al. CNN features off-the-shelf:an astounding baseline for recognition. Computer Vision and Pattern Recognition, 2014:512-519.
    12 Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA. 2014. 580-587.[doi:10.1109/CVPR.2014.81]
    13 Babenko A, Slesarev AV, Chigorin A, et al. Neural codes for image retrieval. In:Fleet D, Pajdla T, Schiele B, Tuytelaars T, eds. Computer Vision - ECCV 2014. Lecture Notes in Computer Science, vol 8689. Springer. Cham. 2014. 584-599.[doi:10.1007/978-3-319-10590-1_38]
    14 Jegou H, Chum O. Negative evidences and co-occurences in image retrieval:The benefit of PCA and whitening. European Conference on Computer Vision. Lecture Notes on Computer Science, vol 7573. Sringer. Berlin, Heidelberg. 2012. 774-787.[doi:10.1007/978-3-642-33709-3_55]
    15 Gong Y, Wang L, Guo R, et al. Multi-scale orderless pooling of deep convolutional activation features. In:Fleet D, Pajdla T, Schiele B, Tuytelaars T, eds. Computer Vision - ECCV 2014. Lecture Notes in Computer Science, vol 8695. Springer, Cham. 2014. 392-407.[doi:10.1007/978-3-319-10584-0_26]
    16 Ng JY, Yang F, Davis LS, et al. Exploiting local features from deep networks for image retrieval. 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Boston, MA, USA. 2015. 53-61.[doi:10.1109/CVPRW.2015.7301272]
    17 Tolias G, Sicre R, Jégou H. Particular object retrieval with integral max-pooling of CNN activations. arXiv preprint arXiv, 2015:1511. 05879.
    18 Babenko A, Lempitsky V. Aggregating local deep features for image retrieval. Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile. 2015. 1269-1277.[doi:10.1109/ICCV.2015.150]
    19 Saxena A, Chung S H, Ng A Y, et al. Learning Depth from Single Monocular Images. Neural Information Processing Systems, 2006:1161-1168.
    20 Saxena A, Sun M, Ng A. Make3D:Learning 3D scene structure from a single still image. IEEE TPAMI, 2009, 31:824-840.[doi:10.1109/TPAMI.2008.132]
    21 Liu B, Gould S, Koller D, et al. Single image depth estimation from predicted semantic labels. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA. 2010. 1253-1260.[doi:10.1109/CVPR.2010.5539823]
    22 Karsch K, Liu C, Kang SB, et al. Depth extraction from video using non-parametric sampling. In:Fitzgibbon A, Lazebnik S, Perona P, et al., eds. Computer Vision - ECCV 2012. Lecture Notes in Computer Science, vol 7576. Springer. Berlin, Heidelberg. 2012. 775-788.[doi:10.1007/978-3-642-33715-4_56]
    23 Konrad J, Wang M, Ishwar P, et al. 2D-to-3D image conversion by learning depth from examples. 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, RI, USA. 2012. 16-22.[doi:10.1109/CVPRW.2012.6238903]
    24 Eigen D, Puhrsch C, Fergus R, et al. Depth Map Prediction from a Single Image using a Multi-Scale Deep Network. Neural Information Processing Systems, 2014:2366-2374.
    25 Liu F, Shen C, Lin G, et al. Deep convolutional neural fields for depth estimation from a single image. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA. 2015. 5162-5170.[doi:10.1109/CVPR.2015.7299152]
    26 Philbin J, Chum O, Isard M, et al. Object retrieval with large vocabularies and fast spatial matching. 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, MN, USA. 2007. 1-8.[doi:10.1109/CVPR.2007.383172]
    27 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv, 2015:1409.1556.
    28 Deng J, Dong W, Socher R, et al. ImageNet:A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA. 2009. 248-255.[doi:10.1109/CVPR.2009.5206848]
    29 He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1904-1916.[doi:10.1109/TPAMI.2015.2389824]
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

申金晟,池明旻.深度先验图像特征在城市遥感大数据中的应用.计算机系统应用,2018,27(9):33-39

复制
分享
文章指标
  • 点击次数:2481
  • 下载次数: 2794
  • HTML阅读次数: 1318
  • 引用次数: 0
历史
  • 收稿日期:2017-12-06
  • 最后修改日期:2017-12-27
  • 在线发布日期: 2018-07-26
文章二维码
您是第11202862位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号