repository
Special Graduate Schools
Graduate School of Information and Communication Technology
Department of Information and Communication
3. Theses(Master)
A Framework for Annotation and Content-Based Multimedia Retrieval System
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 변광준 | - |
| dc.contributor.author | 이수철 | - |
| dc.date.accessioned | 2019-10-21T06:46:20Z | - |
| dc.date.available | 2019-10-21T06:46:20Z | - |
| dc.date.issued | 2005 | - |
| dc.identifier.other | 414 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/16437 | - |
| dc.description | 학위논문(박사)--아주대학교 정보통신전문대학원 :정보통신공학과,2005 | - |
| dc.description.tableofcontents | TABLE OF CONTENTS CHAPTER 1 INTRODUCTION TO MULTIMEDIA INFORMATION RETRIEVAL = 1 1.1 Background and Problem Statement = 1 1.2 State-of-the-Art Status = 2 1.3 Research Objectives and Main Contributions = 2 1.3.1 The XCRAB project = 4 1.4 Dissertation Organization = 6 PART Ⅰ = 7 IMAGE SYSTEM = 7 CHAPTER 2 INTRODUCTION OF IMAGE ANALYSIS AND RETREVAL = 8 2.1 Feature extraction = 9 2.1.1 Color = 10 2.1.2 Texture = 11 2.1.3 Shape = 14 2.1.4 Color layout = 16 2.1.5 Segmentation = 17 2.2 High-Dimensional Indexing = 18 2.2.1 Dimension reduction = 19 2.2.2 Multidimensional indexing techniques = 20 2.3 Image Retrieval System = 21 2.3.1 QBIC = 22 2.3.2 Virage = 22 2.3.3 RetrievalWare = 23 2.3.4 Photobook = 23 2.3.5 VisualSEEk and WebSEEk = 24 2.3.6 Netra = 24 2.3.7 MARS = 25 2.3.8 Other systems = 25 CHAPTER 3 IMAGE ANALYSIS: SPATIAL RELATIONSHIP USED IN THE THESIS = 27 3.1 Image Representation for Spatial Relationship = 27 3.2 Spatial similarity algorithms = 32 3.3 Analysis of spatial information = 34 3.1.1 Symbolic coding of spatial relationships = 35 3.1.2 Metric for measuring spatial similarity an algorithm = 37 CHAPTER 4 REDUCTION RULES FOR SPATIAL RELATIONSHIP = 42 4.1 Definition of spatial relations = 42 4.2 Reduction rules = 45 4.3 Correctness of the rules = 47 4.3.1 Proof of the rules = 50 4.4 Applying reduction rules = 53 4.5 Experimental results = 54 4.5.1 Effectiveness = 56 4.5.2 Efficiency = 60 PART Ⅱ = 63 VIDEO SYSTEM = 63 CHAPTER 5 INTRODUCTION TO VIDEO DATABASE SYSTEMS = 64 5.1 Terminologies = 65 CHAPTER 6 VIDEO ANALYSIS AND VIDEO REPRESENTATION = 68 6.1 Terminologies = 68 6.1.1 Shot boundary detection = 68 6.1.2 Key frame extraction = 69 6.2 Video Representation = 69 6.2.1 Sequential key frame representation = 69 6.2.2 Group-based representation = 70 6.2.3 Scene-based representation = 70 6.2.4 Video mosaic representation = 70 CHAPTER 7 VIDEO RETRIEVAL USING AUDIO FEATURES = 72 7.1 Music and Audio Analysis = 72 7.2.1 Music discrimination = 73 7.2.2 Short time energy function = 74 7.2 FAI Indexing Scheme = 76 7.3 Scene determination = 79 CHAPTER 8 XCRAB: UNIFIED FRAMEWORK FOR MULTIMEDIA RETRIEVAL SYSTEM = 84 8.1 XCRAB image and video search engine = 84 8.1.1 Overview of system architecture = 85 8.1.2 Client application = 87 8.1.3 Server application = 89 8.2 Implementation = 89 CHAPTER 9 CONCLUSION AND FUTURE WORK = 92 BIBLIOGRAPHY = 95 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | A Framework for Annotation and Content-Based Multimedia Retrieval System | - |
| dc.title.alternative | 주석과 내용기반의 멀티미디어 검색시스템을 위한 프레임웍 | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 정보통신전문대학원 | - |
| dc.contributor.alternativeName | 李壽喆 | - |
| dc.contributor.department | 정보통신전문대학원 정보통신공학과 | - |
| dc.date.awarded | 2005. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 564608 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000000414 | - |
| dc.description.alternativeAbstract | In this thesis, a new framework is presented which support the efficient representation, indexing and retrieval of multimedia data by content. Raw multimedia data is assumed to exist in the form of programs that typically consist of a combination of media types such as visual, audio, and text. We partition each such media stream into smaller units based on actual physical events. These physical events within each media stream can then be effectively indexed for retrieval. Research in this area in the past several years has focused on the use of speech recognition and image analysis techniques. As a complimentary effort to the prior work, we will focus on using the associated audio and image information for video analysis. This thesis dedicated to the two of the most important media types: images and videos. Novel approaches to the feature analysis, content representation, indexing and retrieval are presented. The main contributions of this thesis are: (ⅰ) reliable and robust feature extraction and representation techniques for images and videos; (ⅱ) introduction of spatial relationship techniques to image retrieval, which greatly improves the retrieval performance and alleviates user’s query formulation burden; and (ⅲ) introduction several reduction rules for spatial relationships, which improve query processing time and save disk space; (ⅳ) introduction of a new audio feature extraction and analysis techniques for video. Extensive experimental results for large data sets have validated to the proposed approaches. | - |
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