{"id":13572,"date":"2021-10-22T14:41:17","date_gmt":"2021-10-22T06:41:17","guid":{"rendered":"https:\/\/achieve.dhcn.cn\/?p=13572"},"modified":"2024-04-04T01:30:22","modified_gmt":"2024-04-03T17:30:22","slug":"%e5%9f%ba%e4%ba%8e%e5%8d%b7%e7%a7%af%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e7%9a%84%e8%bf%91%e4%bb%a3%e6%8a%a5%e7%ba%b8%e5%b9%bf%e5%91%8a%e5%9b%be%e7%89%87%e8%81%9a%e7%b1%bb%e6%96%b9%e6%b3%95","status":"publish","type":"post","link":"https:\/\/achieve.dhcn.cn\/en\/site\/works\/dhjournal\/202102\/13572.html","title":{"rendered":"\u57fa\u4e8e\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u8fd1\u4ee3\u62a5\u7eb8\u5e7f\u544a\u56fe\u7247\u805a\u7c7b\u65b9\u6cd5"},"content":{"rendered":"

\u4f5c\u8005\uff1a\u738b\u6d77\u6ca3 \u9093\u67ef <\/span>\u9648\u9759\uff1b\u8f6c\u81ea\uff1a\u516c\u4f17\u53f7 DH\u6570\u5b57\u4eba\u6587<\/p>\n\n\n\n

Infrastructure<\/span><\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

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\u9648\u9759 \/ \u5357\u4eac\u5927\u5b66\u827a\u672f\u5b66\u9662<\/span><\/strong><\/p>\n\n\n\n

————————————<\/span><\/p>\n\n\n\n

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\u5173\u952e\u8bcd:<\/span><\/strong>\u8fd1\u4ee3\u62a5\u7eb8\u5e7f\u544a \u5185\u5bb9\u5206\u6790 \u56fe\u50cf\u89e3\u6790 \u5377\u79ef\u795e\u7ecf\u7f51\u7edc \u56fe\u7247\u805a\u7c7b<\/p>\n\n\n\n

————————————<\/span><\/p>\n\n\n\n

\u5f15\u8a00<\/span><\/strong><\/p>\n\n\n\n

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\"\u56fe\u7247\"<\/p>\n\n\n\n

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\u7b2c\u4e8c\u6b65\u662f\u7279\u5f81\u63d0\u53d6\u73af\u8282\uff0c\u4e3b\u8981\u901a\u8fc7\u57fa\u4e8e\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u6df1\u5ea6\u5b66\u4e60\u548c\u8fc1\u79fb\u5b66\u4e60\u6280\u672f\u5c06\u6bcf\u4e2a\u7ecf\u8fc7\u9884\u5904\u7406\u7684\u56fe\u7247\u8f6c\u5316\u4e3a\u4e00\u4e2a\u56fa\u5b9a\u7ef4\u5ea6\u7684\u7279\u5f81\u5411\u91cf\uff0c\u5b9e\u73b0\u56fe\u50cf\u7279\u5f81\u63d0\u53d6\u3002[7]<\/sup>\u6b64\u5904\uff0c\u6211\u4eec\u4f7f\u7528\u7ecf\u7531\u56fe\u7247\u6570\u636e\u5e93ImageNet[8]<\/sup>\u9884\u8bad\u7ec3\u8fc7\u7684VGG16[9]<\/sup>\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u4f5c\u4e3a\u56fe\u7247\u7279\u5f81\u63d0\u53d6\u7684\u4e3b\u8981\u5de5\u5177\u3002VGG16\u662f\u4e3a\u56fe\u50cf\u8bc6\u522b\u53ca\u5206\u7c7b\u4efb\u52a1\u800c\u642d\u5efa\u7684\u4e00\u4e2a\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\uff0c\u662f\u8fd1\u5e74\u6765\u975e\u5e38\u6d41\u884c\u7684\u4e00\u4e2a\u56fe\u50cf\u5904\u7406\u6df1\u5ea6\u5b66\u4e60\u6784\u67b6\u3002VGG16\u5171\u753116\u5c42\u7f51\u7edc\u6784\u6210\uff0c\u56fe3\u7ed9\u51fa\u4e86\u5176\u7f51\u7edc\u7ed3\u6784\u7684\u57fa\u672c\u6784\u67b6\u3002\u8be5\u7f51\u7edc\u4ee5224\u00d7224\u50cf\u7d20\u7684RGB\u56fe\u7247\u4e3a\u8f93\u5165\uff0c\u901a\u8fc7\u7531\u5377\u79ef\u5c42(convolution)\u548c\u6c60\u5316\u5c42(max pooling)\u6784\u6210\u768413\u5c42\u7f51\u7edc\u4e0d\u65ad\u8fdb\u884c\u5377\u79ef\u64cd\u4f5c\uff0c\u5c06\u7ef4\u5ea6\u4e3a224\u00d7224\u00d73\u7684\u539f\u59cb\u8f93\u5165\u56fe\u7247\u9010\u6b65\u8f6c\u5316\u4e3a\u4e00\u4e2a\u7ef4\u5ea6\u4e3a7\u00d77\u00d7512\u7684\u7279\u5f81\u5f20\u91cf\uff0c\u7136\u540e\u518d\u901a\u8fc73\u4e2a\u5168\u8fde\u63a5\u5c42\u8fdb\u4e00\u6b65\u5c06\u56fe\u7247\u7684\u7279\u5f81\u4fe1\u606f\u8f6c\u5316\u4e3a\u4e00\u4e2a\u7ef4\u5ea6\u4e3a1000\u7684\u7279\u5f81\u5411\u91cf\uff0c\u5e76\u6700\u7ec8\u901a\u8fc7\u4e00\u4e2asoftmax\u5c42\u5b9e\u73b0\u56fe\u50cf\u5206\u7c7b\u529f\u80fd\uff0c\u6574\u4e2a\u7f51\u7edc\u5171\u5305\u542b\u7ea61.3\u4ebf\u4e2a\u81ea\u7531\u53c2\u6570\u3002\u5f53\u5168\u90e8\u7f51\u7edc\u53c2\u6570\u88ab\u786e\u5b9a\u4ee5\u540e\uff0c\u4efb\u4f55\u4e00\u4e2a\u5c3a\u5bf8\u4e3a224\u00d7224\u50cf\u7d20\u7684RGB\u56fe\u7247\u901a\u8fc7\u591a\u5c42\u7f51\u7edc\u7684\u5c42\u5c42\u8fed\u4ee3\u8ba1\u7b97\uff0c\u5c06\u8f93\u51fa\u4e00\u4e2a\u5728\u56fe\u50cf\u7c7b\u522b\u7a7a\u95f4\u4e0a\u7684\u6982\u7387\u5206\u5e03\uff0c\u4ece\u800c\u5b9e\u73b0\u56fe\u50cf\u8bc6\u522b\u5206\u7c7b\u3002<\/p>\n\n\n\n

\u7136\u800c\uff0c\u8981\u8ba9\u8fd9\u79cd\u89c4\u6a21\u5e9e\u5927\u7684\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u771f\u6b63\u53d1\u6325\u4f5c\u7528\uff0c\u9700\u8981\u4f7f\u7528\u6d77\u91cf\u7684\u5e26\u6709\u5206\u7c7b\u6807\u8bb0\u7684\u56fe\u50cf\u6570\u636e\u8fdb\u884c\u53c2\u6570\u8bad\u7ec3\uff0c\u800c\u4e14\u53c2\u4e0e\u8bad\u7ec3\u7684\u56fe\u50cf\u6700\u597d\u4e0e\u9700\u8981\u8fdb\u884c\u5904\u7406\u7684\u56fe\u50cf\u5177\u6709\u8f83\u597d\u7684\u76f8\u4f3c\u6027\u3002\u56e0\u800c\uff0c\u5728\u7406\u60f3\u60c5\u51b5\u4e0b\uff0c\u4e3a\u4e86\u8bad\u7ec3VGG\u7f51\u7edc\u5bf9\u8fd1\u4ee3\u62a5\u7eb8\u5e7f\u544a\u56fe\u7247\u8fdb\u884c\u8bc6\u522b\uff0c\u6211\u4eec\u5e94\u8be5\u6536\u96c6\u5927\u91cf\u7684\u8fd1\u4ee3\u62a5\u7eb8\u5e7f\u544a\u56fe\u7247\uff0c\u5bf9\u5b83\u4eec\u8fdb\u884c\u5206\u7c7b\uff0c\u5e76\u7ed9\u5c5e\u4e8e\u540c\u4e00\u7c7b\u7684\u6bcf\u5f20\u56fe\u7247\u6253\u4e0a\u4e00\u4e2a\u76f8\u540c\u7684\u7c7b\u522b\u6807\u7b7e\u3002\u6bd4\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4eba\u5de5\u5f52\u7c7b\u7684\u65b9\u6cd5\u628a\u5bf9\u5e94\u4e8e\u540c\u4e00\u5219\u5e7f\u544a\u7684\u4e0d\u540c\u5e7f\u544a\u56fe\u7247\u5f52\u4e3a\u4e00\u7c7b\uff0c\u5e76\u6253\u4e0a\u8be5\u7c7b\u7684\u6807\u8bb0\u3002\u4f46\u662f\uff0c\u7531\u4e8e\u8fd1\u4ee3\u62a5\u7eb8\u5e7f\u544a\u56fe\u7247\u7684\u7279\u6b8a\u6027\uff0c\u8fd9\u79cd\u505a\u6cd5\u5e76\u4e0d\u73b0\u5b9e\u3002\u9996\u5148\uff0c\u300a\u4e2d\u56fd\u5546\u4e1a\u5e7f\u544a\u6863\u6848\u5e93\u300b\u4e2d\u6240\u6536\u5f55\u7684\u5e7f\u544a\u56fe\u7247\u6570\u91cf\u8fbe\u523067,000\u4efd\u4e4b\u591a\uff0c\u4f9d\u9760\u4eba\u5de5\u5f52\u7c7b\u6253\u6807\u5728\u77ed\u65f6\u95f4\u5185\u96be\u4ee5\u505a\u5230\u3002\u5176\u6b21\uff0c\u5c3d\u7ba1\u6863\u6848\u5e93\u4e2d\u768467,000\u4efd\u5e7f\u544a\u56fe\u7247\u6570\u76ee\u5df2\u7ecf\u6bd4\u8f83\u5927\u4e86\uff0c\u4f46\u662f\u5bf9\u4e8e\u8bad\u7ec3VGG16\u8fd9\u6837\u7684\u5927\u89c4\u6a21\u7f51\u7edc\u4ecd\u65e7\u8fdc\u8fdc\u4e0d\u591f\u3002\u8fd9\u610f\u5473\u7740\u901a\u8fc7\u81ea\u5efa\u8bad\u7ec3\u6570\u636e\u96c6\u6765\u8bad\u7ec3VGG16\u7f51\u7edc\u662f\u4e0d\u73b0\u5b9e\u7684\uff0c\u6211\u4eec\u5fc5\u987b\u91c7\u7528\u5176\u4ed6\u65b9\u6cd5\u89e3\u51b3\u7f51\u7edc\u7684\u8bad\u7ec3\u95ee\u9898\u3002\u201c\u8fc1\u79fb\u5b66\u4e60\u201d(transfer learning)\u662f\u89e3\u51b3\u8fd9\u7c7b\u95ee\u9898\u7684\u4e00\u4e2a\u6709\u6548\u624b\u6bb5\u3002<\/p>\n\n\n\n

\"\"
\u56fe3 VGG16\u7f51\u7edc\u6784\u67b6\u793a\u610f\u56fe[10]<\/sup><\/figcaption><\/figure><\/div>\n\n\n\n

<\/p>\n\n\n\n

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\u4e09\u3001\u5b9e\u9a8c\u9a8c\u8bc1<\/span><\/strong><\/p>\n\n\n\n

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\"\"
\u88682 \u8fd1\u4ee3\u62a5\u7eb8\u5e7f\u544a\u56fe\u7247\u805a\u7c7b\u65b9\u6cd5\u7684\u6d4b\u8bc4\u548c\u6bd4\u8f83<\/figcaption><\/figure><\/div>\n\n\n\n

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\u56db\u3001\u5b9e\u9645\u5e94\u7528<\/span><\/strong><\/p>\n\n\n\n

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\"\"
\u56fe4 \u8fd1\u4ee3\u62a5\u7eb8\u5e7f\u544a\u56fe\u7247\u805a\u7c7b\u7ed3\u679c <\/figcaption><\/figure><\/div>\n\n\n\n

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\u603b\u7ed3<\/span><\/strong><\/p>\n\n\n\n

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———————————————————————————————————————————————————————————————————<\/p>\n\n\n\n

Clustering Methods of Modern Newspaper Advertisements via Convolutional Neural Network<\/strong><\/p>\n\n\n\n

Wang Haifeng, Deng Ke, Chen Jing<\/p>\n\n\n\n

Abstract: <\/strong><\/p>\n\n\n\n

As important historical documents, commercial advertisements in pre-modern newspapers have received more and more attentions from humanists. Content analysis of mass advertisement images in pre-modern newspaper has becoming an active research direction in contemporary humanities. Most existing algorithms process the advertisement images one by one separately. However, due to the special characteristics of advertisement images in pre-modern newspapers, directly applying image recognition to implement content analysis for a single advertisement image often suffers from noticeable errors, and it typically needs a lot of manpower to verify the results, leading to high financial and operational costs. In this article, we propose to abandon the conventional strategy to implement content analysis for individual advertisement images and consider the problem in a global view. We develop in this study a highly efficient tool by measuring the similarity of different advertisement images based on their feature vectors obtained from deep learning. Applying the proposed strategy to process the advertisement images archived in the China Commercial Advertisement Archive<\/em>, we improved the research efficiency by nearly 6 times. <\/p>\n\n\n\n

Keywords: <\/strong><\/p>\n\n\n\n

Modern Newspaper Advertisements; Content Analysis; Image Processing; Convolutional Neural Network; Clustering Analysis<\/p>\n\n\n\n

———————————————————————————————————————————————————————————————————<\/p>\n\n\n\n

\u7f16 \u8f91  |  \u8d75\u8587<\/p>\n\n\n\n

\u672c\u6587\u4e3a\u56fd\u5bb6\u793e\u4f1a\u79d1\u5b66\u57fa\u91d1\u91cd\u5927\u9879\u76ee\u201c\u57fa\u4e8e\u5927\u6570\u636e\u6280\u672f\u7684\u53e4\u5178\u6587\u5b66\u7ecf\u5178\u6587\u672c\u5206\u6790\u4e0e\u7814\u7a76\u201d(18ZDA238)\u3001\u56fd\u5bb6\u81ea\u7136\u79d1\u5b66\u57fa\u91d1\u8d44\u52a9\u9879\u76ee(11771242)\u3001\u4e2d\u592e\u9ad8\u6821\u57fa\u672c\u79d1\u7814\u4e1a\u52a1\u8d39\u4e13\u9879\u8d44\u91d1\u8d44\u52a9\u9879\u76ee(011514370105)\u548c\u5317\u4eac\u667a\u6e90\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\u7814\u7a76\u5458\u4e13\u9879\u57fa\u91d1\u8d44\u52a9\u9879\u76ee(BAAI2019ZD0103)\u9636\u6bb5\u6027\u6210\u679c\u3002<\/p>\n\n\n\n

\u6ce8\u91ca\uff1a<\/span><\/strong><\/p>\n\n\n\n

[1]\u9648\u5e73\u539f:\u300a\u5de6\u56fe\u53f3\u53f2\u4e0e\u897f\u5b66\u4e1c\u6e10:\u665a\u6e05\u753b\u62a5\u7814\u7a76\u300b\uff0c\u5317\u4eac:\u751f\u6d3b\u00b7\u8bfb\u4e66\u00b7\u65b0\u77e5\u4e09\u8054\u4e66\u5e97\uff0c2018\u5e74\u3002<\/p>\n\n\n\n

[2]B. Mittler,\u201cDefy(N)ing Modernity: Women in Shanghai’s Early News-Media (1872-1915),\u201dJindai Zhongguo funu<\/em>\u0308<\/em> shi yanjiu <\/em>(Research on the History of Women in Modern China<\/em>), November 2013, p.217.<\/p>\n\n\n\n

[3]C.Henriot\uff0c\u201cVisualisingChina,1845-1965:MovingandStillImagesinHistoricalNarratives,\u201dChinesesStudies<\/em>, Leiden: Brill, 2012.<\/p>\n\n\n\n

[4]\u9ec4\u514b\u6b66:\u300a\u4ece\u7533\u62a5\u533b\u836f\u5e7f\u544a\u770b\u6c11\u521d\u4e0a\u6d77\u7684\u533b\u7597\u6587\u5316\u4e0e\u793e\u4f1a\u751f\u6d3b\uff0c1912\u20141926\u300b\uff0c\u300a\u4e2d\u7814\u9662\u8fd1\u4ee3\u53f2\u7814\u7a76\u6240\u96c6\u520a\u300b1988\u5e74\u7b2c17\u671f\u3002<\/p>\n\n\n\n

[5]Tani E. Barlow\uff0c\u201c Buying In: Advertising and the Sexy Modern Girl Icon in Shanghai in the 1920s and 1930s,\u201d The Modern Girl Around the World<\/em>, ed. The Modern Girl Around the World Group, 2008, pp. 288-316.<\/p>\n\n\n\n

[6]The Ephemera Project,\u201cChinese Commercial Advertisement Archive (CCAA),\u201dRice University, Luce Foundation and Nanjing University, https:\/\/ccaa.nju.edu.cn\/html\/index.html<\/a>.<\/p>\n\n\n\n

[7]Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton,\u201cImagenet Classification with Deep Convolutional Neural Networks,\u201dIn Advances in Neural Information Processing Systems, 2012, pp. 1097-1105; C. Szegedy et al., “Going Deeper with Convolutions,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015, pp. 1-9, doi: 10.1109\/CVPR.2015.7298594; K. He et al., “Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770-778, doi: 10.1109\/CVPR.2016.90.<\/p>\n\n\n\n

[8]J. Deng et al.,\u201cImageNet: A Large-scale Hierarchical Image Database,\u201d2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.<\/p>\n\n\n\n

[9]Karen Simonyan, Andrew Zisserman,\u201cVery Deep Convolutional Networks for Large-Scale Image Recognition,\u201dIn International Conference on Learning Representations (ICLR), 2015.<\/p>\n\n\n\n

[10]Image source: https:\/\/heuritech.wordpress.com\/2016\/02\/29\/a-brief-report-of-the-heuritech-deep- learning-meetup-5\/<\/a>.<\/p>\n\n\n\n

[11]X. Lv, Z. J. Wang\uff0c\u201c Compressed Binary Image Hashes Based on Semisupervised Spectral Embedding,\u201d IEEE Transactions on Information Forensics and Security, 2013, vol. 8, no. 11, pp. 1838-1849.<\/p>\n\n\n\n

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