图像搜索引擎的功能与存在的风险
摘要
图像搜索引擎,即互联网中以获取图像信息为目标的专业搜索引擎。在“读图时代”,用户所能接触到的可视化信息达到了前所未有的量级,传统的搜索方式已无法满足用户需求,可高效获取信息的图像搜索引擎应运而生。
出处
《青年记者》
北大核心
2020年第26期107-108,共2页
Youth Journalist
二级参考文献66
-
1Andreopoulos A, Tsotsos J K. 50 years of objectrecognition: Directions forward [J]. Computer Vision andImage Understanding, 2013,117(8) : 827-891.
-
2Russakovsky 0,Deng Jia, Su Hao,et al. ImageNet: Largescale visual recognition challenge [J]. International Journalof Computer Vision,2015,115(3) : 211-252.
-
3Zhou Bolei,Lapedriza A,Xiao Jianxiong,et al. Learningdeep features for scene recognition using Places database [C]//Proc of the 28th Annual Conf on Neural InformationProcessing Systems. Cambridge, MA: MIT Press, 2014:487-495.
-
4Xiao Jianxiong,Hays J, Ehinger K,et ai. Sun database:Large-scale scene recognition from abbey to zoo [C] //Proc ofthe IEEE Conf on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE, 2015 : 3485-3492.
-
5Krizhevsky A, Sutskever I, Hinton G E. ImageNetclassification with deep convolutional neural networks [C] //Proc of the 26th Annual Conf on Neural InformationProcessing Systems. Cambridge MA: MIT Press, 2012 :1097-1105.
-
6Yosinski J,Clune J, Bengio Y, et al. How transferablefeatures in deep neural networks [C] //Proc of the 28thAnnual Conf on Neural Information Processing Systems.Cambridge, MA: MIT Press, 2014 : 3320-3328.
-
7Zeiler M D, Fergus R. Visualizing and understandingconvolutional networks [C] //Proc of the 16th European Confon Computer Vision. Berlin: Springer, 2014? 297-312.
-
8Simonyan K, Zisserman A. Very deep convolutionalnetworks for large-scale image recognition [J], CoRR abs/1409.1556, 2014.
-
9Szegedy C,Liu Wei, Jia Yangqing,et al. Going deeper withconvolutions [C] //Proc of the IEEE Conf on ComputerVision and Pattern Recognition. Piscataway,NJ: IEEE,2015: 1-9.
-
10Donahue J, Jia Yangqing, Vinyals 0,et al. DeCAF : A deepconvolutional activation feature for generic visual recognition[C] //Proc of the 31st Int Conf on Machine Learning. NewYork: ACM, 2014: 647-655.
共引文献92
-
1宫福敏.露天煤矿大型结构件智能双丝焊修研究[J].工矿自动化,2021,47(S01):116-118.
-
2郭雄伟,冯俊优,赵睿,丁志刚.基于图像识别的胶带撕裂检测系统研究[J].煤炭工程,2021,53(S01):86-90. 被引量:2
-
3王铁君,王维兰.基于本体的唐卡图像标注方法[J].吉林大学学报(工学版),2020,50(1):289-296. 被引量:2
-
4吴建宝,肖诗斌,王焕鹏.改进的神经网络算法在舰船目标识别上的应用[J].北京信息科技大学学报(自然科学版),2019,34(3):94-98. 被引量:4
-
5袁欣.计算机图像识别的智能化处理技术瓶颈与突破[J].电脑编程技巧与维护,2016(8):83-84. 被引量:12
-
6肖辉,郏涛,薄非.广播电视台融合媒体跨屏智能识别互动技术研究[J].广播与电视技术,2017,44(1):25-30. 被引量:1
-
7武煜博.图像识别技术发展与应用[J].电子技术与软件工程,2017(4):86-86. 被引量:9
-
8曹永峰,赵燕君.基于GA-BP神经网络的计算机智能化图像识别技术探究[J].应用激光,2017,37(1):139-143. 被引量:25
-
9庞俊震,淮永建.基于移动端的月季花快速识别方法研究[J].计算机应用与软件,2017,34(8):36-41. 被引量:1
-
10周晔,张军平.基于多尺度深度学习的商品图像检索[J].计算机研究与发展,2017,54(8):1824-1832. 被引量:12