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研究生： 李鎮宇
Chen-Yu Lee
論文名稱： 使用智慧型代理人的語意基礎影像檢索系統
Semantic Based Image Annotation and Retrieval Systems Using Intelligent Agents
指導教授： 蘇豐文
Von-Wun Soo
口試委員:
學位類別： 博士
Doctor
系所名稱： 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年： 2005
畢業學年度： 94
語文別： 英文
論文頁數： 180
中文關鍵詞： 語意網 、智慧型代理人 、本體論 、影像檢索
外文關鍵詞： Semantic Web, Intelligence Agent, Ontology, Image retrieval
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    • 目前，在影像檢索系統(Image retrieval system) 的發展上大致可分為兩大類，基於關鍵字的影像檢索系統(Keyword-based image retrieval system) 與基於內容分析的影像檢索系統(content-based image retrieval system) 。其中，雖然關鍵字的影像檢索系統已被廣泛使用，但是此兩種影像檢索系統仍然有著低準確性(low retrieval precision)與不容易使用等關鍵的問題。為了解決這個問題，我們提出了一個基於語意的影像加註與檢索系統(Semantic-based image annotation and retrieval system) 。

    為了建立能夠使用語意的影像檢索系統，我們建構了一個基於語意的影像檢索架構，並且使用了語意網(Semantic Web) 、本體論(Ontology) 、中文詞庫 (Thesaurus) 、基於經驗的推論法(Case-based reasoning) 等相關技術來實現之。但我們亦發現，影像的加註(Annotation)問題將會是影響使用語意影像檢索系統的主要關鍵之一。因此，針對影像加註方面我們也應用了智慧型代理人(Intelligence Agent)的技術與資料探勘(Data mining)的技術來建立加註引導代理人(Annotation Guide Agent)與建構領域常識(Domain commonsense) ，以幫助加註者加註，並亦提出解決衝突(Conflict)問題。最後從一系列的實驗中我們證實了語意影像檢索系統的精確性，確認在代理人的技術下可以幫助加註者在短時間內達成更完整與更正確的加註。

    Image retrieval (IR) research has been ongoing for sometime. Two major paradigms in IR are developed respectively: Keyword-based metadata image retrieval and content-based image retrieval. The low retrieval precision and the difficulty of formulating an exact feature query are the major drawbacks of these approaches. To overcome these drawbacks, we propose a semantic-based image annotation and retrieval approach. In other words, we annotate images to be retrieved with semantic tags in a standard and uniform representation (RDF) that are defined and derived from thesaurus and domain concepts called domain ontology in form of OWL, so that the information retrieval can be conducted to some extent at the abstract “semantic” level. We also integrated various techniques, such as semantic web, case-based reasoning and complex matching algorithms to establish the system.

    Further, we realized that it is a difficult goal to achieve a complete annotation, therefore, we used the technique of intelligent agents to designed an annotator guide agent (AGA), who could guide an annotator to decide what to annotate for an image in a more effective and coherent manner with suggesting critical properties and domain commonsense. We also devised conflict detection patterns based on different data, ontology at different inference levels and proposed the corresponding automatic conflict resolution strategies. Finally, we conducted several experiments to compare the performance of the semantic-based retrieval, AGA, and automatic conflict resolution. The experiments showed that the proposed method improved the performance significantly.

    Content Chapter 1 Introduction 1 Chapter 2 Image annotation and retrieval system 5 2.1 Content based image retrieval system 5 2.1.1. Related works 5 2.1.2. Advantages and drawbacks of CBIR system 6 2.2 Keyword based image annotation and retrieval system 6 2.2.1. Related works 6 2.2.2. Advantages and drawbacks of KBIR systems 8 2.3 Semantic based image annotation and retrieval system 9 2.3.1. Why not a hybrid IR system? 9 2.3.2. The problems that a SBIR system could face 9 Chapter 3 Background and fundamental concepts 11 3.1 Semantic Web 11 3.1.1. Resource Description Framework (RDF) 12 3.1.2. Resource Description Framework Schema (RDFS) 12 3.2 Ontology 13 3.2.1. Web Ontology Language (OWL) 14 3.2.2. Domain ontology 15 3.2.3. The lack of logic representation in original OWL/DAML 17 3.3 Domain knowledge 19 3.3.1. Commonsense 19 3.3.2. Association rule 21 3.3.3. The mandarin Chinese thesaurus 25 3.4 An Annotator model 27 3.4.1. Domain knowledge 27 3.4.2. The annotator model 28 Chapter 4 A semantic based image annotation and retrieval system 33 4.1 Introduction and related works 33 4.2 Framework overview 33 4.3 Image representation 34 4.4 Query expression 34 4.5 Image annotation 36 4.6 Automatic indexing and structural matching 38 Chapter 5 A natural language case based reasoning approach in expressing query and annotation 41 5.1 Introduction and related works 41 5.2 Case based reasoning 41 5.2.1. The pre-processor module 42 5.2.2. The similarity module 43 5.2.3. The prediction module 43 5.2.4. The memory update module 44 5.3 The alignment algorithms 44 5.3.1. The MCS algorithm 44 5.3.2. The MLRM algorithm 46 5.4 Knowledge merging 48 5.4.1. The separation module 49 5.4.2. The combination module 52 5.5 Experiments and results 53 5.5.1. Results of similarity match in CBL 53 5.5.2. Results of automatic semantic annotation 55 5.6 Discussions 60 Chapter 6 Image annotation and an annotation guide agent 61 6.1 Guiding human annotators 63 6.1.1. Guiding the annotation direction 64 6.1.2. Complete annotations 66 6.2 Related works 70 6.3 Critical property 71 6.3.1. Degree of scatter-ness 72 6.3.2. Commonsense inducibility 77 6.3.3. The query effect statistics based on ontology 79 6.3.4. Normalization and balancing 82 6.3.5. A simple scenario 82 6.4 Conflicts handler 85 6.5 Experiments and results 86 6.5.1. Behavior analysis of annotators and image-seekers 87 6.5.2. Determination of the thresholds for commonsense 91 6.5.3. The weighting of the three factors for a critical property 93 6.5.4. The comparison of the performance 94 6.6 Discussions 100 Chapter 7 Conflict detection and resolution 105 7.1 Fundamental concepts and issues 105 7.1.1.The time that conflicts happened 105 7.1.2. A right annotation in different view 106 7.1.3. Assumptions 107 7.1.4. The reliability of knowledge and an annotator 108 7.2 Related works 121 7.3 The conspicuous conflicts 122 7.3.1. The completeness of conspicuous conflicts 123 7.3.2. Data conflicts 123 7.3.3. Data conflict detection and resolution 115 7.3.4. Ontology (schema/structure) conflicts 118 7.4 The inconspicuous conflicts 121 7.4.1. The completeness of inconspicuous conflicts 121 7.4.2. The five inconspicuous conflicts 122 7.4.3. The detection and resolution of inconspicuous conflicts 127 7.4.4. Prevent the interactions conflicts among conflict resolutions 132 7.5 Experiments and results 134 7.5.1. The initial settings for annotation experiments 134 7.5.2. Performance evaluation experiments 136 7.6 Discussions 140 Chapter 8 Conclusion and future work 143 8.1 Conclusion 143 8.2 Future work 145 8.2.1. Ontology manipulation 145 8.2.2. Effective Case Based NL translator 146 8.2.3. Personalization 146 8.2.4. Conflicts handling 147 Bibliographic 149 Appendix 161 Appendix A 161 Appendix B 168 Appendix C 172 Appendix D 175 Appendix E 177 Appendix F 178 Appendix G 180

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