Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19318
DC FieldValueLanguage
dc.contributor廖宜恩zh_TW
dc.contributor陳彥錚zh_TW
dc.contributor楊東麟zh_TW
dc.contributor.advisor高勝助zh_TW
dc.contributor.advisorShang-Juh Kaoen_US
dc.contributor.author孟憲君zh_TW
dc.contributor.authorMeng, Shian-Jiunen_US
dc.contributor.other中興大學zh_TW
dc.date2007zh_TW
dc.date.accessioned2014-06-06T07:06:31Z-
dc.date.available2014-06-06T07:06:31Z-
dc.identifierU0005-0307200617243700zh_TW
dc.identifier.citation[1] Alexander Maedche and Steffen Staab, “Ontology Learning for the Semantic Web”, IEEE INTELLIGENT SYSTEMS, 2001. [2] E.L. van den Broek, L.G. Vuurpijl, P. Kisters, and J.C.M. von Schmid ,“Content-Based Image Retrieval: Color-selection exploited”, Nijmegen Institute for Cognition and Information, 2002. [3] A.Th.(Guus) Schreiber, Barbara Dubbeldam, Jan Wielemaker, and Bob ielinga, “Ontology-Based Photo Annotation”, IEEE INTELLIGENT SYSTEMS, 2001. [4] Director of the World Wide Web Consortium, http://www.w3.org/People/Berners-Lee/ [5] Michael C. Daconta, Leo J. Obrst and Kevin T. Smith, “The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management”, pp.9-15,ISBN:0471432571. [6] Graham Klyne and Jeremy Carroll, “Resource Description Framework (RDF) Concepts and Abstract Syntax”, W3C Proposed Recommendation, http://www.w3.org/TR/rdf-concepts/, January 23, 2003. [7] Deborah McGuinness and Frank van Harmelen, “OWL Web Ontology Language Overview”, W3C Proposed Recommendation, http://www.w3.org/TR/owl-features/ , August 18, 2003. [8] Jena - A Semantic Web Framework for Java, http://jena.sourceforge.net/ [9] Irena Valova, Boris Rachev, “Retrieval by Color Features in Image Databases”, Budapest, Hungary, Septemper, 2004. [10] Picture Finder Online-Demo, http://134.102.206.80:8080/demo/frameset.jsp/ [11] A.Th.(Guus) Schreiber, Barbara Dubbeldam, Jan Wielemaker, and Bob ielinga, “Ontology-Based Photo Annotation”, IEEE INTELLIGENT SYSTEMS, 2001. [12] A.M. Tam and C.H.C. Leung, “Structured Natural- Language Description for Semantic Content Retrieval”, J. American Soc. Information Science, Sept. 2001. [13] Laura Hollink, Guus Schreiber, Jan Wielemaker, “Semantic Annotation of Image Collections”, Proceedings of the K-CAP 2003 Workshop on Knowledge Markup, 2003. [14] Eero Hyvonen, Hannu Erki, “Ontology-Based Image Annotation and Retrieval”, University of Helsinki Dept. of Computer Science Helsinki 27th April 2005. [15] A lexical database for the English language, http://wordnet.princeton.edu/ [16] Lei Zhang, Fuzong Lin, Bo Zhang,“SUPPORT VECTOR MACHINE LEARNING FOR IMAGE RETRIEVAL”, Department of Computer Science and Technology, Tsinghua University, Beijing, 2001. [17] Zaher AGHBARI, Akifumi MAKINOUCHI, “Semantic Approach to Image Database Classification and Retrieval”, E.E., Kyushu University, 2003. [18] Y.Deng, B.S.Manjunath and H.Shin , "Color Image Segmentation", Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR ''99, Fort Collins, CO, vol.2, pp.446-51, June 1999. [19] JSEG algorithm, http://vision.ece.ucsb.edu/segmentation/jseg/software/ [20] DAMIAN CONWAY, “An Experimental Comparison of Three Natural Language Colour Naming Models”, Proc. East- West International Conference on Human-Computer Interaction, St. Petersburg, Russia, pp. 328-39, 1992. [21] WordNet API, http://sourceforge.net/projects/jwordnet/zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/19318-
dc.description.abstract在現今這個資訊爆發的時代,多媒體影像的資料量與日俱增,如何在充滿各式各樣的圖形中找出使用者真正想要的實在是目前刻不容緩的課題。由於以往以內容為基礎的影像搜尋技術缺乏語意上的支援,常造成搜尋結果並非如使用者所預期,本篇論文提出了在影像中加入語意的支援,使系統對影像能具有更深入的理解,進而提高對影像搜尋的正確性。 本論文基於本體論為基礎,發展出一套具有半自動化標記影像的系統實作。首先,我們利用相似顏色為同一群的分類方法來對影像加以切割。切割完後的每一個區域,可以擷取其顏色的特徵並把這些特徵加入到我們的本體論中。接著,我們就可以只針對我們喜歡的物件加以標記,然後便可以利用我們建立好的本體知識庫所提供空間上的關連性,以及辭彙網路所提供語義上的同義詞類與上義詞類的特性,進而使我們的系統有效的描述影像各物件中動作的行為與空間上的關係。最後,我們針對三種不同的搜尋方式:以關鍵字為基礎、以空間關係為基礎、以簡易自然語言包含空間與動作關係為基礎的方式,作為效能上的比較報告。zh_TW
dc.description.abstractAs the volume of multimedia images increase drastically on the web, it is important and becoming difficult to search for a targeted picture efficiently. Commonly used content-based image retrieval is short of semantic support. The retrieved resources may be far away from user''s expectation. In this paper, we propose a new image retrieval system incorporated semantics to make the system better in image repository, consequently, to search for an image more accurate. Based on the construction of spatial ontology, we implement a semi-automatic annotation images system. Firstly, images are segmented according to the variations in color. We extract each segmented region, record its color feature and make annotations in the ontology file. Accordingly, we are able to apply the spatial relationships from the established ontology along with synonym and hypernym characteristics from the WordNet for efficient image retrieved. Finally we compare and report the performance of three different searching mechanisms: keyword-based, spatial relationship-based, and simple natural language and action behavior relationship-based.en_US
dc.description.tableofcontents中文摘要 i Abstract ii 1. 緒論 1 1.1 前言 1 1.2 研究動機與方法 1 1.3 論文架構 5 2. 相關研究 6 2.1 語意網(Semantic Web) 6 2.1.1 語意網基本結構 8 2.1.2 語意網各層介紹 8 2.1.3 Jena推理引擎 12 2.2 相關影像搜尋技術 16 2.2.1 基於影像內容的搜尋方法 16 2.2.2 基於本體論影像搜尋方法 17 2.3 詞彙網路(WordNet) 19 3. 系統設計與架構 20 3.1 系統簡介 20 3.2 系統架構 22 3.2.1 影像標記結構 23 3.2.2 影像查詢結構 32 4. 系統展示與實驗結果 38 4.1實驗展示 39 4.2 影像標記實驗 39 4.3 影像搜尋實驗 42 4.3.1 關鍵字查詢 42 4.3.2 空間關係查詢 43 4.3.3 簡易自然語言查詢 46 4.4 實驗結果比較 50 5. 結論與未來展望 51 5.1 結論 51 5.2 未來展望 52 參考文獻 53zh_TW
dc.language.isoen_USzh_TW
dc.publisher資訊科學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0307200617243700en_US
dc.subjectSemantic weben_US
dc.subject語意網zh_TW
dc.subjectImage searchen_US
dc.subjectOWLen_US
dc.subjectWordNeten_US
dc.subjectOntologyen_US
dc.subject影像搜尋zh_TW
dc.subjectOWLzh_TW
dc.subject詞彙網路zh_TW
dc.subject本體論zh_TW
dc.title基於空間關係之語意影像搜尋系統zh_TW
dc.titleSemantic Image Search Based on Spatial Relationshipsen_US
dc.typeThesis and Dissertationzh_TW
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