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系統識別號 U0026-2805201211373400
論文名稱(中文) 語意導向影像管理實現方法與技術研發
論文名稱(英文) Development of Enabling Methods and Technologies for Semantic-oriented Image Management
校院名稱 成功大學
系所名稱(中) 製造資訊與系統研究所
系所名稱(英) Institute of Manufacturing Information and Systems
學年度 100
學期 2
出版年 101
研究生(中文) 石琢暐
研究生(英文) Cho-Wei Shih
學號 p98971036
學位類別 博士
語文別 英文
論文頁數 80頁
口試委員 指導教授-陳裕民
口試委員-歐陽超
口試委員-王昌斌
口試委員-朱治平
口試委員-陳昭和
口試委員-曾新穆
中文關鍵字 影像管理  語意導向影像管理  影像知識模式  影像生命週期 
英文關鍵字 Semantic-oriented image management  Image management  Image knowledge model  Image life-cycle 
學科別分類
中文摘要 科技發展與知識經濟加速了資訊、知識與數位內容的流通,雖有助於使用者便利地取得資訊與知識,卻也同時造成資訊過載(Information overload)之現象。資訊過載係指資訊量過多,造成使用者難以適時、確切、迅速地找到需要的資訊,因此,提供一個有效的管理模式,以協助使用者適時獲得並整合正確的資訊,實有其必要性。再者,多數使用者傾向採用影像來記錄生活中的點滴,舉凡人、事、地或物等值得記錄的時刻皆然;且影像可傳達的隱性資訊(包括情感、回憶)較聲音直覺與豐富。是故,影像內容的管理模式更顯其重要性,卻存在幾個關鍵問題:(1)缺乏一般化的影像管理模式;(2)未考量影像之特殊性;(3)忽略影像內隱的語意資訊。
有鑑於此,本研究參考「知識管理(Knowledge management)」之模式與方法,針對影像的生命週期(Image lifecycle),提出一「語意導向影像管理模式」,再依此設計「語意導向影像管理系統架構」與開發「實現技術(Enabling technologies)」,以實現「影像知識管理」之目標。
為實現研究目標,本研究訂立之研究項目如下:
(1) 影像生命週期分析與設計;
(2) 一般化影像管理模式設計;
(3) 語意導向影像管理模式設計;
(4) 語意導向影像管理功能定義與設計;
(5) 語意導向影像管理系統模組開發;
(6) 語意導向影像管理系統整合與測試;
(7) 系統評量(實驗設計)。
英文摘要 The rapid development of technology and advent of knowledge economy accelerate the circulation and access of digital content. However, it also increases the difficulty of knowledge acquirement due to information overload. Therefore, it is necessary to provide an effective management method for information gathering and integration.
Images are widely used to record the happenings of life (including people, events, places or things) due to its ease of use and rich containment of implicit information (including emotions and memories). Therefore, image management has become a remarkable requirement, however, it still exists issues of: (a) lacking of generic image management model; (b) ignoring the characteristics of images; (c) ignoring the implicit semantics of images.
To fulfill the requirements of image management, this research will study lifecycle of images and then propose a "semantic-oriented image management model" based on the concepts and methods of “knowledge management”. System framework and its enabling technologies will also be developed according to the proposed model.
To achieve the objective, the research tasks include:
(a) Design and analysis of image lifecycle,
(b) Design of generic image management model,
(c) Design of semantic-oriented image management model
(d) Planning and design of system framework
(e) Development of enabling technologies and system modules, and
(f) Experiments and assessment.
論文目次 摘要 I
ABSTRACT II
ACKNOWLEDGMENTS III
TABLE OF CONTENTS IV
LIST OF FIGURES VI
LIST OF TABLES VIII
CHAPTER 1. INTRODUCTION 1
1.1. BACKGROUND 1
1.2. MOTIVATION 2
1.3. RESEARCH OBJECTIVES 3
CHAPTER 2. SEMANTIC-ORIENTED IMAGE MANAGEMENT MODEL AND SYSTEM FRAMEWORK 5
2.1. DESIGN OF SEMANTIC-ORIENTED IMAGE MANAGEMENT MODEL 5
2.2. SCENARIO FOR SEMANTIC-ORIENTED IMAGE MANAGEMENT 7
2.3. DESIGN OF SEMANTIC-ORIENTED IMAGE MANAGEMENT SYSTEM FRAMEWORK 8
2.4. RESEARCH ISSUES 11
2.5. DESIGN OF FUNCTIONAL MODULE AND ENABLING TECHNOLOGY 12
CHAPTER 3. ENABLING TECHNOLOGY - IMAGE SEEKING USING INFORMATION NEEDS RADAR MODEL 14
3.1. INTRODUCTION 14
3.2. INFORMATION NEEDS ANALYSIS AND DEFINITION 15
3.3. SYSTEM ARCHITECTURE 20
3.3.1. Image gathering module 20
3.3.2. Radar modeling module 21
3.3.3. Image filtering module 26
3.4. EXPERIMENTAL RESULTS AND ANALYSIS 26
3.4.1. Results and analysis: filter thresholds and data size 27
3.4.2. Results and analysis: comparison between ours and others 30
3.5. SUMMARY 32
CHAPTER 4. ENABLING TECHNOLOGY - IMAGE ANNOTATION BASED ON FRACTAL FEATURES 33
4.1. INTRODUCTION 33
4.2. FRACTAL IMAGE CODING 35
4.2.1. Basic fractal image coding 35
4.2.2. No search fractal image coding 36
4.3. SYSTEM ARCHITECTURE 38
4.3.1. Feature extraction 38
4.3.2. Classifier training 40
4.3.3. Label assignment 41
4.4. EXPERIMENTAL RESULTS AND ANALYSIS 41
4.4.1. Results: selection of color and texture features 42
4.4.2. Results: correlation of visual features 42
4.4.3. Results: Accuracy of image annotation 45
4.4.4. Results: influence of fractal feature 47
4.4.5. Results: influence of thresholds 48
4.4.6. Results: influence of labels 49
4.5. SUMMARY 50
CHAPTER 5. ENABLING TECHNOLOGY - SELF-TUNING IMAGE CLASSIFICATION USING A CONFIDENCE LEVEL 51
5.1. INTRODUCTION 51
5.2. THE CONCEPT OF CONFIDENCE INDEX 53
5.3. SYSTEM ARCHITECTURE 55
5.3.1. Concept extraction 56
5.3.2. Category assignment 56
5.3.3. Variant detection 58
5.3.4. Category adjustment 59
5.4. EXPERIMENTAL RESULTS AND ANALYSIS 60
5.4.1. The influence and experimental results of parameter 61
5.4.2. Comparison between CEIC and others 62
5.4.3. The trends of Confidence Index (CI) and Similarity Index (SI) 63
5.5. SUMMARY 66
CHAPTER 6. CONCLUSION AND FUTURE RESEARCH 67
6.1. CONCLUSION 67
6.2. FUTURE RESEARCH 68
REFERENCES 70
VITA 78
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