政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136927

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136927

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 113648/144635 (79%)
造访人次 : 51606589 在线人数 : 813

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 文學院 > 圖書資訊與檔案學研究所 > 學位論文 > Item 140.119/136927

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/136927

题名:	基於自動情境標註之圖像檢索工具發展與數位人文應用研究 Developing an Image Retrieval Tool based on Automatic Context Annotation for Digital Humanities Research
作者:	趙映翔 Zhao, Ying-Xiang
贡献者:	陳志銘 Chen, Chih-Ming 趙映翔 Zhao, Ying-Xiang
关键词:	數位人文圖像檢索語意鴻溝物件標註情境標註深度學習自動圖像標註 Mask R-CNN TF-IDF SVM 行為分析 Digital humanities Image retrieval Semantic gap Object annotation Contextual annotation Deep learning Automatic image annotation Mask R-CNN TF-IDF SVM Behavioral analysis
日期:	2021
上传时间:	2021-09-02 16:36:05 (UTC+8)
摘要:	「圖像檢索」在資訊蓬勃發展的現代，已經成為數位人文研究的重要方式之一。而影響傳統「基於文本的圖像檢索工具(Text-Based Image Retrieval, TBIR)」之圖像檢索效能的主要問題，為人工所注入代表圖像後設資料(metadata)與使用者所下檢索詞之間的語意鴻溝(semantic gap)。隨著電腦視覺技術快速發展而衍伸出的自動圖像標註(automatic image annotation)，由機器為其自動添加後設資料以降低的語意鴻溝。然而自動圖像標註的物件標註僅能找到具有該物件特徵，對於使用者的圖像檢索及圖像理解的幫助有限，進而促成本研究探索「自動情境標註」為減少圖像情境與人之間的語意鴻溝，並發展出得以有效輔助人文學者進行圖像檢索及圖像解讀之數位人文工具。因此，本研究發展出「基於自動情境標註之圖像檢索工具(Image Retrieval Tool Based on Automatic Context Annotation, IRT-ACA)」。該系統的核心技術採用Mask R-CNN、TF-IDF及SVM，主要目的為圖像中的實體物件識別，以及抽象的情境識別，並將所得之數據以標籤化形式提供使用者用於圖像檢索與瀏覽，讓使用者得以可以快速萃取數位圖像中的實體物件以及抽象情境之訊息。進而促進人文學者更有效率地解讀圖像情境。為驗證本研究發展之IRT-ACA是否有助於人文學者進行圖像解讀，本研究採用實驗研究法之對抗平衡設計，將實驗對象分為兩組，根據不同的系統使用順序來依次操作「IRT-ACA」與「基於文本的圖像檢索工具(Text-Based Image Retrieval, TBIR)」來完成檢索任務學習單。並透過行為歷程記錄來完整記錄實驗對象的系統操作行為、科技接受度問卷來反映實驗對象對於系統的實際感受，以及半結構式訪談來瞭解實驗對象的想法與建議，透過多種方法進行交互驗證，以瞭解本研究發展之IRT-ACA與TBIR在自動情境標註之準確度、解讀圖像情境之成效以及科技接受度上的差異。研究結果發現：第一，IRT-ACA的自動情境標註準確度已足以有效輔助使用者解讀圖像情境；第二，使用TBIR與IRT-ACA在解讀圖像情境之成效上達顯著差異，並且IRT-ACA顯著優於TBIR；第三，使用TBIR與IRT-ACA在整體科技接受度上達顯著差異，並且IRT-ACA顯著優於TBIR，但其中的系統易用性未達顯著差異。從訪談分析中顯示，實驗對象對於兩個系統的操作難意度及使用流暢性上均感到滿意，因此給予系統易用性分數差異不大；第四，IRT-ACA的標籤型檢索比起自由下達檢索詞的檢索更能促進實驗對象的檢索意願；第五，使用IRT-ACA高分組使用者之檢索行為更充分使用到所有檢索功能；第六，IRT-ACA使用者之查看圖像至筆記紀錄之轉移率高於TBIR。 Image retrieval has become one of the significant approaches in digital humanities research in the digital age. The main problem affecting the performance of Text-Based Image Retrieval (TBIR) is the semantic gap between the manually determined metadata for images and the users’ search terms or keywords. With the rapid development of computer vision technology in recent years, automatic image annotation developed by machine learning schemes can reduce the semantic gap between humans through automatically adding metadata based on the identified image objects’ tags. However, the object tags determined by automatic image annotation can only find the characteristics of image objects, which is of little help to users’ image retrieval and image comprehension because they are still too low level from human’s perspectives. It prompted this research to develop automatic context annotation as a digital humanities tool that can effectively assist humanities scholars in image context interpretation by reducing the semantic gap between the subject of the image context and humans. Therefore, this research developed an Image Retrieval Tool Based on Automatic Context Annotation (IRT-ACA). The core technology of the tool is Mask R-CNN, TF-IDF, and SVM, which aims to identify physical objects and abstract contexts hidden in images and provide users with more rich metadata in the form of object and contextual tags for image retrieval and browsing, so that users can quickly extract needed information from images, thus facilitating the more efficient interpretation of image contexts by humanists. To verify whether IRT-ACA developed in this research is beneficial to humanities scholars in image interpretation, this research utilized a counterbalanced design of the experimental research method to examine the research questions. Users were divided into two groups and operated the IRT-ACA and the TBIR tool alternately to complete the two designed image retrieval tasks. Besides, the behavioral history recorder was used to record the system operation behavior of the users using the IRT-ACA and the TBIR completely. The technology acceptance questionnaire was used to reflect the actual feelings and perceptron of the experimental subjects towards the two systems. The semi-structured interview was used to understand the thoughts, ideas, and suggestions of the users who alternately used the two image retrieval systems. The research results are summarized as follows. First, the accuracy of the automatic context annotation of IRT-ACA was sufficient to interpret the image context effectively. Second, there was a significant difference in the effectiveness of interpreting the image context between TBIR and IRT-ACA as well as IRT-ACA is significantly superior to TBIR. Third, there was a significant difference in the overall technology acceptance and perceived usefulness between TBIR and IRT-ACA as well as IRT-ACA is significantly superior to TBIR, but there was no significant difference in the perceived ease of use between TBIR and IRT-ACA. The analysis results show that users were satisfied with the ease of operation and the smoothness of using the two systems, so the difference in the scores of perceived ease of use was not significant. Fourth, the labeled retrieval provided by the IRT-ACA promoted the research subjects’ willingness to retrieve more than the free retrieval of using keywords. Fifth, the retrieval behaviors of the IRT-ACA users with high image interpretation performance made full use of all retrieval functions, including contextual tags’ search, object tags’ search, full-text search, and title research. Sixth, the transfer rate from viewing images to take notes of the users who used IRT-ACA is higher than that of users who used TBIR.
參考文獻:	陳勇汀（2017）。行為順序檢定：滯後序列分析/ Behavior Analysis: Lag Sequential Analysis。檢自https://pulipulichen.github.io/HTML-Lag-Sequential-Analysis/ 項潔、陳麗華（2014）。數位人文－學科對話與融合的新領域。數位人文研究與技藝（頁9-23） Agosti, M., Ferro, N., Orio, N., & Ponchia, C. (2014). CULTURA outcomes for improving the user’s engagement with cultural heritage collections. Procedia Computer Science, 38, 34-39. doi:10.1016/j.procs.2014.10.007 Beaudoin, J. E. (2014). A framework of image use among archaeologists, architects, art historians and artists. Journal of Documentation, 70(1), 119-147. doi:10.1108/JD-12-2012-0157 Beaudoin, J. E., & Brady, J. E. (2011). Finding visual information: a study of image resources used by archaeologists, architects, art historians, and artists. Art Documentation: Journal of the Art Libraries Society of North America, 30(2), 24-36. doi:10.1086/adx.30.2.41244062 Bradshaw, B. (2000). Semantic based image retrieval: a probabilistic approach. In Proceedings of the eighth ACM international conference on Multimedia, 167-176. doi:10.1145/354384.354456 Brooks, J. (2019). COCO Annotator. Jsbroks/coco-annotator. https://github.com/jsbroks/coco-annotator/ Burdescu, D. D., Mihai, C. G., Stanescu, L., & Brezovan, M. (2013). Automatic image annotation and semantic based image retrieval for medical domain. Neurocomputing, 109, 33-48. doi:10.1016/j.neucom.2012.07.030 Carneiro, G., Chan, A. B., Moreno, P. J., & Vasconcelos, N. (2007). Supervised learning of semantic classes for image annotation and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3), 394-410. doi:10.1109/TPAMI.2007.61 Chen, C.-M., & Tsay, M.-Y. (2017). Applications of collaborative annotation system in digital curation, crowdsourcing, and digital humanities. The Electronic Library, 35(6), 1122-1140. doi:10.1108/EL-08-2016-0172 Chen, S. H., & Chen, Y. H. (2017). A content-based image retrieval method based on the google cloud vision api and wordnet. Intelligent Information and Database Systems, 651-662. doi:10.1007/978-3-319-54472-4_61 Chen, Y., Zhou, X. S., & Huang, T. S. (2001). One-class SVM for learning in image retrieval. In Proceedings 2001 International Conference on Image Processing, IEEE 2001, 34-37. doi:10.1109/ICIP.2001.958946 Cheng, Q., Zhang, Q., Fu, P., Tu, C., & Li, S. (2018). A survey and analysis on automatic image annotation. Pattern Recognition, 79, 242-259. doi:10.1016/j.patcog.2018.02.017 Eakins, J., & Graham, M. (1999). Content-based image retrieval. Gordo, A., Almazán, J., Revaud, J., & Larlus, D. (2016). Deep image retrieval: learning global representations for image search. Computer Vision – ECCV 2016, 9910, 241-257. doi:10.1007/978-3-319-46466-4_15 Hare, J. S., Lewis, P. H., Enser, P. G., & Sandom, C. J. (2006). Mind the gap: another look at the problem of the semantic gap in image retrieval. In Multimedia Content Analysis, Management, and Retrieval 2006, 6073, 607309.1-607309.12. doi:10.1117/12.647755 He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, CVPR 2017, 2961-2969. doi:10.1109/ICCV.2017.322 Henningsmeier, J. (1998). The foreign sources of Dianshizhai huabao 點石齋畫報, A nineteenth century Shanghai illustrated magazine. Ming qing yanjiu, 7(1), 59-91. doi:10.1163/24684791-90000374 Huang, C., Xu, H., Xie, L., Zhu, J., Xu, C., & Tang, Y. (2018). Large-scale semantic web image retrieval using bimodal deep learning techniques. Information Sciences, 430-431, 331-348. doi:10.1016/j.ins.2017.11.043 Huang, Z., Zhong, Z., Sun, L., & Huo, Q. (2019). Mask R-CNN with pyramid attention network for scene text detection. In 2019 IEEE Winter Conference on Applications of Computer Vision, IEEE 2019, 764-772. doi:10.1109/WACV.2019.00086 Hwang, G. J., Yang, L. H., & Wang, S. Y. (2013). A concept map-embedded educational computer game for improving students’ learning performance in natural science courses. Computers & Education, 69, 121-130. doi:10.1016/j.compedu.2013.07.008 Hyvönen, E., Saarela, S., Styrman, A., & Viljanen, K. (2003). Ontology-based image retrieval. In Proceedings of XML Finland Conference, 27-51. Retrieved from https://seco.cs.aalto.fi/publications/2002/hyvonen-styrman-saarela-ontology-based-image-retrieval-2002.pdf Ivasic-Kos, M., Ipsic, I., & Ribaric, S. (2015). A knowledge-based multi-layered image annotation system. Expert Systems with Applications, 42(24), 9539-9553. doi:10.1016/j.eswa.2015.07.068 Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105. https://doi.org/10.1145/3065386 Li, Z., & Tang, J. (2015). Weakly supervised deep metric learning for community-contributed image retrieval. IEEE Transactions on Multimedia, 17(11), 1989-1999. doi:10.1109/TMM.2015.2477035 Liu, Y., Zhang, D., & Lu, G. (2008). Region-based image retrieval with high-level semantics using decision tree learning. Pattern Recognition, 41(8), 2554-2570. doi:10.1016/j.patcog.2007.12.003 Liu, Y., Zhang, D., Lu, G., & Ma, W. Y. (2007). A survey of content-based image retrieval with high-level semantics. Pattern Recognition, 40(1), 262-282. doi:10.1016/j.patcog.2006.04.045 Llamas, J., Lerones, P. M., Zalama, E., & Gómez-García-Bermejo, J. (2016). Applying deep learning techniques to cultural heritage images within the inception project. Progress in Cultural Heritage: Documentation, Preservation, and Protection, 25-32. doi:10.1007/978-3-319-48974-2_4 Lorang, E., Soh, L.-K., Datla, M. V., & Kulwicki, S. (2015). Developing an image-nased classifier for detecting poetic content in historic newspaper collections. D-Lib Magazine, 21(7/8). doi:10.1045/july2015-lorang Lowe, D. G. (1999). Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision, IEEE 1999, 1150-1157. doi:10.1109/ICCV.1999.790410 Murthy, V. N., Maji, S., & Manmatha, R. (2015). Automatic image annotation using deep learning representations. In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, 603-606. doi:10.1145/2671188.2749391 Murthy, V. S. V. S., Vamsidhar, E., Kumar, J. S., & Rao, P. S. (2010). Content based image retrieval using Hierarchical and K-means clustering techniques. International Journal of Engineering Science and Technology, 2(3), 209-212. Nguyen, H. V., & Bai, L. (2011). Cosine similarity metric learning for face verification. In Computer Vision – ACCV 2010, 709-720. doi:10.1007/978-3-642-19309-5_55 Özyurt, F., Tuncer, T., Avci, E., Koç, M., & Serhatlioğlu, İ. (2019). A novel liver image classification method using perceptual hash-based convolutional neural network. Arabian Journal for Science and Engineering, 44(4), 3173-3182. doi:10.1007/s13369-018-3454-1 Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR 2016, 779-788. doi: 10.1109/CVPR.2016.91 Rui, Y., & Huang, T. (2000). Optimizing learning in image retrieval. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000, 236-243. doi: 10.1109/CVPR.2000.855825 Schreibman, Susan. (2012). Digital humanities: centres and peripheries. Historical Social Research-Historische Sozialforschung, 37(3), 46-58. Shyu, C. R. (2000). Relevance feedback decision trees in content-based image retrieval. In 2000 Proceedings Workshop on Content-based Access of Image and Video Libraries, 68-72. Su, J. H., Wang, B. W., Yeh, H. H., & Tseng, V. S. (2009). Ontology-based semantic web image retrieval by utilizing textual and visual annotations. In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, 425-428. Milan, Italy: IEEE. doi:10.1109/WI-IAT.2009.317 Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1349-1380. doi:10.1109/34.895972 Terras, M. (2012). Image processing and digital humanities. Digital Humanities in Practice, 71-90. Facet. Retrieved from http://discovery.ucl.ac.uk/1327983/ Vijayarajan, V., Dinakaran, M., Tejaswin, P., & Lohani, M. (2016). A generic framework for ontology-based information retrieval and image retrieval in web data. Human-Centric Computing and Information Sciences, 6(1), 18. doi:10.1186/s13673-016-0074-1 Wan, H. L., & Chowdhury, M. (2003). Image semantic classification by using SVM. Journal of software, 14(11), 1891-1899. Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., ... & Liu, W. (2018). Cosface: Large margin cosine loss for deep face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR 2018, 5265-5274. doi:10.1109/CVPR.2018.00552 Wang, L. Z., & Gu, X. E. (2007). Dian-Shi-Zhai pictorial suiting both refined and popular tastes. Journal of Shanxi Normal University (Social Science Edition), 4. Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of machine learning research, 10(2). Xiaoqing, Y. (2003). The Dianshizhai pictorial: Shanghai urban life, 1884-1898 (No. 98). Ann Arbor, MI: University of Michigan Press. Yin, S., Chen, W., & Qin, X. (2009). Research on semantic network image retrieval method. In 2009 International Conference on Future BioMedical Information Engineering, 449-452. doi: 10.1109/FBIE.2009.5405823 Zhang, D., Islam, Md. M., & Lu, G. (2012). A review on automatic image annotation techniques. Pattern Recognition, 45(1), 346-362. doi:10.1016/j.patcog.2011.05.013 Zhang, Y. J. (2006). Semantic-based visual information retrieval. Pennsylvania, PA: IGI Global.
描述:	碩士國立政治大學圖書資訊與檔案學研究所 108155017
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0108155017
数据类型:	thesis
DOI:	10.6814/NCCU202101403
显示于类别:	[圖書資訊與檔案學研究所] 學位論文

文件中的档案:

档案	描述	大小	格式	浏览次数
501701.pdf		3516Kb	Adobe PDF2	2	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈