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題名 運用詮釋資料之線上社群圖片搜尋系統
Design and Implementation of an Annotation-Based Image Search System for Online Communities作者 范佐玄
Fan, Zuo Syuan貢獻者 圖書館
陳恭
Chen, Kung
范佐玄
Fan, Zuo Syuan關鍵詞 圖片搜尋
詮釋資料
image metadata
image annotations
image search日期 2013 上傳時間 1-十月-2014 13:40:58 (UTC+8) 摘要 近年搜尋系統的技術日漸完善，不但查詢速度快且精準度也提高，但搜尋的目標內容多是以文字為主。在圖片搜尋這項任務中，傳統的圖片搜尋系統大致分為兩種方法：一是採用低階的圖形識別方式，或是採用高階的文字內容，利用文字標籤等資訊來協助辨識圖片內容。 本論文運用特定結構的詮釋資料(Annotation Data) 來協助辨識圖片內容，有別於文字標籤和圖片的簡單連結，我們採用的詮釋資料可以加註在照片上的特定區域，並針對該區域內的圖片內容歸屬類別以及補充文字說明。本論文並實作了一個線上社群圖片搜尋系統，提供線上社群針對有添加這種詮釋資料的圖片進行搜尋。 本圖片搜尋系統除了讓使用者能夠簡單輸入關鍵字來尋找照片之外，並能夠限制關鍵字對應的類別是人物或是時、地、物不同屬性，還會根據照片庫內的資料關聯性來針對使用者輸入的關鍵字推薦相關的內容。 參考文獻 [1] George W. Furnas , Caterina Fake , Luis von Ahn , Joshua Schachter , Scott Golder , Kevin Fox , Marc Davis , Cameron Marlow , Mor Naaman, Why do tagging systems work?, CHI `06 extended abstracts on Human factors in computing systems, April 22-27, 2006, Montréal, Québec, Canada.[2] Lassila, O., Swick, R. (1998). The Resource Description Framework (RDF) Model & Syntax. W3C Working Draft. http://www.w3.org/TR/WD-rdf-syntax. [3] Stefan Decker, Dan Brickley, Janne Saarela, Jürgen Angele, A Query and Inference Service for RDF, W3C Query Languages Workshop (1998), http://purl.org/net/rdf/papers/QL98-queryservice[4] B. McBride, "Jena: A Semantic Web Toolkit", IEEE Internet Computing, November - December 2002.[5] Ariel Ortiz, Web development with python and django, Proceedings of the 43rd ACM technical symposium on Computer science education. ACM, 2012. P. 686.[6] Datta, R., Joshi, D., Li, J. and Wang, J., Image Retrieval: Ideas, Influences, and Trends of the New Age. in ACM Computing Surveys, (2008).[7] Yan, R., Natsev, A., and Campbell, M. 2007., An efficient manual image annotation approach based on tagging and browsing. In Workshop on Multimedia Information Retrieval on the Many Faces of Multimedia Semantics. ACM, New York, 13–20.[8] A. Leff and J. Rayfield "Web-application development using the Model/View/Controller design pattern", Proc. Int. Enterprise Distrib. Object Comput. Conf., pp.118 -127 2001[9] Lavrenko, V., R. Manmatha, and J. Jeon: 2004, ‘A model for learning the semantics of pictures’. In: Advances in Neural Information Processing Systems 16.[10] A. Goodrum. Image information retrieval: An overview of current research. Informing Science, 3(2):63–67, 2000.[11] Blei, D. M. and M. I. Jordan: 2003, ‘Modeling annotated data’. In: Proc. ACM SIGIR. pp. 127–134.[12] Carneiro, G. and N. Vasconcelos: 2005a, ‘A database centric view of semantic image annotation and retrieval’. In: SIGIR. pp. 559 – 566.[13] Makadia, A.; Pavlovic, V.; and Kumar, S. 2010. Baselines for image annotation. In IJCV, 90(1):88–105.[14] S.-J. P. Jae-Hun Choi, Seong-Hee Park, "Design and imple-mentation of a concept-based image retrieval system with edge description templates," in SPIE Storage and Retrieval Methods and Application for Multmedia, vol. 5307, 2004, pp. 571–581.[15] Wilson, G., Aruliah, D., Brown, C.T., Hong, N.P.C., Davis, M., Guy, R.T., Haddock, S.H., Huff, K., Mitchell, I.M., Plumbley, M.D., et al. (2012). Best practices for scientific computing.[16] Google. Protocol Buffers: Google`s Data Interchange Format. Documentation and open source release, http://code.google.com/p/protobuf/[17] Datta, Ritendra, Joshi, Dhiraj, Li, Jia, Wang, James Z., 2008. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 40 (2) (Article 5).[18] P. Enser, Visual image retrieval: Seeking the alliance of concept-based and content-based paradigms, Journal of Information Science 26(4) (2000) 199–210. 描述 碩士 資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100753027 資料類型 thesis dc.contributor 圖書館 en dc.contributor.advisor 陳恭 zh_TW dc.contributor.advisor Chen, Kung en_US dc.contributor.author (作者) 范佐玄 zh_TW dc.contributor.author (作者) Fan, Zuo Syuan en_US dc.creator (作者) 范佐玄 zh_TW dc.creator (作者) Fan, Zuo Syuan en dc.date (日期) 2013 - dc.date.accessioned 1-十月-2014 13:40:58 (UTC+8) - dc.date.accessioned 6-十一月-2014 14:16:42 (UTC+8) - dc.date.available 1-十月-2014 13:40:58 (UTC+8) - dc.date.available 6-十一月-2014 14:16:42 (UTC+8) - dc.date.issued (上傳時間) 1-十月-2014 13:40:58 (UTC+8) - dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/70310 - dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/71136 - dc.description (描述) 碩士 en dc.description.abstract (摘要) 近年搜尋系統的技術日漸完善，不但查詢速度快且精準度也提高，但搜尋的目標內容多是以文字為主。在圖片搜尋這項任務中，傳統的圖片搜尋系統大致分為兩種方法：一是採用低階的圖形識別方式，或是採用高階的文字內容，利用文字標籤等資訊來協助辨識圖片內容。 本論文運用特定結構的詮釋資料(Annotation Data) 來協助辨識圖片內容，有別於文字標籤和圖片的簡單連結，我們採用的詮釋資料可以加註在照片上的特定區域，並針對該區域內的圖片內容歸屬類別以及補充文字說明。本論文並實作了一個線上社群圖片搜尋系統，提供線上社群針對有添加這種詮釋資料的圖片進行搜尋。 本圖片搜尋系統除了讓使用者能夠簡單輸入關鍵字來尋找照片之外，並能夠限制關鍵字對應的類別是人物或是時、地、物不同屬性，還會根據照片庫內的資料關聯性來針對使用者輸入的關鍵字推薦相關的內容。 en dc.description.tableofcontents 第一章 1緒論 11.1 前言 11.2研究之背景 11.2.1 圖片搜尋(Image Search) 11.2.2 詮釋資料(Annotations) 21.2.3 資源描述框架(Resource Description Framework，RDF) 41.3 研究動機與目的 51.4 論文研究成果 61.5 論文之章節架構 6第二章 7相關研究的概況與評述 72.1 圖片搜尋的相關研究 72.2 提供圖片標記與搜尋的線上社群服務 82.2.1 Facebook 82.2.2 Flickr 9第三章 11研究方法與架構 113.1 系統架構 113.2 系統設計概念 113.3 系統設計元件 12第四章 15系統實作與使用者情境 154.1 實作方法 154.1.1 詮釋資料的格式定義與說明 154.1.2 系統運作流程 174.2 使用者情境 204.2.1 照片的資料編輯介面 204.2.2 照片的搜尋介面 214.2.3 搜尋結果的呈現與建議搜尋介面 23第五章 25結論與未來發展 255.1 結論 255.2 未來發展 25參考文獻 27 zh_TW dc.format.extent 1587540 bytes - dc.format.extent 1587540 bytes - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.language zh_TW en dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100753027 en_US dc.subject (關鍵詞) 圖片搜尋 en dc.subject (關鍵詞) 詮釋資料 en dc.subject (關鍵詞) image metadata en dc.subject (關鍵詞) image annotations en dc.subject (關鍵詞) image search en dc.title (題名) 運用詮釋資料之線上社群圖片搜尋系統 zh_TW dc.title (題名) Design and Implementation of an Annotation-Based Image Search System for Online Communities en dc.type (資料類型) thesis en dc.relation.reference (參考文獻) [1] George W. Furnas , Caterina Fake , Luis von Ahn , Joshua Schachter , Scott Golder , Kevin Fox , Marc Davis , Cameron Marlow , Mor Naaman, Why do tagging systems work?, CHI `06 extended abstracts on Human factors in computing systems, April 22-27, 2006, Montréal, Québec, Canada.[2] Lassila, O., Swick, R. (1998). The Resource Description Framework (RDF) Model & Syntax. W3C Working Draft. http://www.w3.org/TR/WD-rdf-syntax. [3] Stefan Decker, Dan Brickley, Janne Saarela, Jürgen Angele, A Query and Inference Service for RDF, W3C Query Languages Workshop (1998), http://purl.org/net/rdf/papers/QL98-queryservice[4] B. McBride, "Jena: A Semantic Web Toolkit", IEEE Internet Computing, November - December 2002.[5] Ariel Ortiz, Web development with python and django, Proceedings of the 43rd ACM technical symposium on Computer science education. ACM, 2012. P. 686.[6] Datta, R., Joshi, D., Li, J. and Wang, J., Image Retrieval: Ideas, Influences, and Trends of the New Age. in ACM Computing Surveys, (2008).[7] Yan, R., Natsev, A., and Campbell, M. 2007., An efficient manual image annotation approach based on tagging and browsing. In Workshop on Multimedia Information Retrieval on the Many Faces of Multimedia Semantics. ACM, New York, 13–20.[8] A. Leff and J. Rayfield "Web-application development using the Model/View/Controller design pattern", Proc. Int. Enterprise Distrib. Object Comput. Conf., pp.118 -127 2001[9] Lavrenko, V., R. Manmatha, and J. Jeon: 2004, ‘A model for learning the semantics of pictures’. In: Advances in Neural Information Processing Systems 16.[10] A. Goodrum. Image information retrieval: An overview of current research. Informing Science, 3(2):63–67, 2000.[11] Blei, D. M. and M. I. Jordan: 2003, ‘Modeling annotated data’. In: Proc. ACM SIGIR. pp. 127–134.[12] Carneiro, G. and N. Vasconcelos: 2005a, ‘A database centric view of semantic image annotation and retrieval’. In: SIGIR. pp. 559 – 566.[13] Makadia, A.; Pavlovic, V.; and Kumar, S. 2010. Baselines for image annotation. In IJCV, 90(1):88–105.[14] S.-J. P. Jae-Hun Choi, Seong-Hee Park, "Design and imple-mentation of a concept-based image retrieval system with edge description templates," in SPIE Storage and Retrieval Methods and Application for Multmedia, vol. 5307, 2004, pp. 571–581.[15] Wilson, G., Aruliah, D., Brown, C.T., Hong, N.P.C., Davis, M., Guy, R.T., Haddock, S.H., Huff, K., Mitchell, I.M., Plumbley, M.D., et al. (2012). Best practices for scientific computing.[16] Google. Protocol Buffers: Google`s Data Interchange Format. Documentation and open source release, http://code.google.com/p/protobuf/[17] Datta, Ritendra, Joshi, Dhiraj, Li, Jia, Wang, James Z., 2008. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 40 (2) (Article 5).[18] P. Enser, Visual image retrieval: Seeking the alliance of concept-based and content-based paradigms, Journal of Information Science 26(4) (2000) 199–210. zh_TW
