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題名 影像內容檢索中以社群網絡演算法為基礎之多張影像搜尋
Query by Multiple Images for Content-Based Image Retrieval Based on Social Network Algorithms
作者 張瑋鈴
Chang, Wei Ling
貢獻者 沈錳坤
Shan, Man Kwan
張瑋鈴
Chang, Wei Ling
關鍵詞 影像內容檢索
多張影像查詢
社群網絡
Content-Based Image Retrieval
Multiple Images Search
Social Network
日期 2011
上傳時間 30-Oct-2012 11:28:22 (UTC+8)
摘要 近年來，隨著數位科技快速的發展，影像資料量迅速的增加，因此影像檢索成為重要的多媒體技術之一。在傳統的影像內容檢索技術中，使用影像低階特徵值，例如顏色(Color)、紋理(Texture)、形狀(Shape)等來描述影像的內容並進行圖片相似度的比對。然而，傳統的影像內容檢索僅提供單張影像查詢，很少研究多張影像的查詢。因此，本研究提出一個可針對多張影像查詢的方法以提供多張影像查詢的影像內容檢索。本研究將影像內容檢索結合社群網絡演算法，使用MPEG-7中相關特徵描述子和SIFT做為主要特徵向量，擷取影像的低階影像特徵，透過特徵相似度計算建立影像之間的網絡，並利用社群網絡演算法找出與多張查詢影像相似的影像。實驗結果顯示所提出的方法可精確的擷取到相似的影像。
In recent years, with the faster and faster development of computer technology, the number of digital images is grown rapidly so that the Content-Based Image Retrieval has become one of important multimedia technologies. Much research has been done on Content-Based Image Retrieval. However, little research has been done on query by multiple images. This thesis investigates the mechanism for query by multiple images.
First, MPEG-7 image features and SIFT are extracted from images. Then, we calculate the similarity of images to construct the proximity graph which represents the similarity structure between images. Last, processing of query by multiple images is achieved based on the social network algorithms. Experimental results indicate the proposed method provides high accuracy and precision.
參考文獻 [1] M. Bober, “MPEG-7 Visual Shape Descriptors,” IEEE Transactions on Circuits and System for Video Technology, Vol. 11, No. 6, pp. 716-719, 2001.
[2] C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Blobworld: Image Segmentation using Expectation-Maximization and Its Application to Image Querying,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 8, pp. 1026–1038, 2002.
[3] S. F. Chang, T. Sikora, and A. Puri, “Overview of MPEG-7 Standard,” IEEE Transactions on Circuits Systems for Video Technology, Vol. 11, No. 6, 2001.
[4] M. Flickner, H. Sawhney, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D.Lee, D. Petkovic, D. Steele, and P. Yanker, “Query By Image and Video Content : The QBIC System,” IEEE Computer Magazine, Vol. 28, No. 9, pp. 23–32, 1995.
[5] A. Gupta, “Visual Information Retrieval: A Virage Perspective,” Virage, Inc. , San Mateo, Calif., 1995.
[6] J. Han, and K. K. Ma, “Fuzzy Color Histogram and Its Use in Color Image Retrieval,” IEEE Transactions on Image Processing, Vol. 11, No. 8, pp. 944-952, 2002.
[7] R. Hess, “An Open-Source SIFT Library,” In Proc. of the ACM International Conference on Multimedia, pp.25–29, 2010.
[8] T. S. Huang, S. Mehrotra, and K. Ramachandran, “Multimedia Analysis and Retrieval System (MARS) Project,” In Proc. of 33rd Annual Clinic on Library Application of Data Processing-Digital Image Access and Retrieval, 1996.
[9] E. Kasutani, A. Yamada, “The MPEG-7 Color Layout Descriptor: a Compact Image Feature Description for High-speed Image/Video Segment Retrieval,” In Proc. of International Conference on Image Processing, pp. 674-677, 2001.
[10] H. K. Kim, J. D. Kim, D. G. Sim, and D. I. Oh, “A Modified Zernike Moment Shape Descriptor Invariant to Translation, Rotation and Scale for Similarity-Based Image Retrieval,” In Proc. of the IEEE International Conference on Multimedia and Expo, pp. 307-310, 2000.
[11] J. J. Koenderink, “The Structure of Images,” Biological Cybernetics, Vol. 50, No.
5, pp. 363-396, 1984.
[12] H. J. Lin, Y. T. Kao, S. H. Yen, and C. J. Wang, “A Study of Shape-Based Image Retrieval,” In Proc. of 24th International Conference on Distributed Computing Systems Workshops, pp. 118-123, 2004.
[13] T. Lindeberg, “Scale-space Theory: A Basic Tool for Analyzing Structures at Different Scales,” Journal of Applied Statistics, Vol. 21, No. 2, pp. 224-270, 1994.
[14] D. Lowe, “Distinctive Image Features from Scale-invariant Keypoints,” International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004.
[15] B. S Manjunath, G. M. Haley, and D. F. Dunn, “Efficient Gabor Filter Design for Texture Segmentation,” Pattern Recognition, Vol. 29, No. 12, pp. 2005-2016, 1996.
[16] B. S. Manjunath, J.-R. Ohm, V. V. Vasudevan, and A. Yamada, “MPEG-7 Color and Texture Descriptors,” IEEE Transactions on Circuit and System for Video Technoloy, Vol. 11, No. 6, pp. 703-715, 2001.
[17] B. S. Manjunath, P. Salembier, and T. Sikora,” Introduction to MPEG-7: Multimedia Content Description Standard,” New York: Wiley, 2001.
[18] J. M. Martinez, “Standards-MPEG-7 Overview of MPEG-7 Description tools, Part 2,” IEEE Multimedia, Vol. 9, No. 3, pp. 83-93, 2002.
[19] W. Niblack, R. Barber, W. Equitz, et al , “The QBIC Project: Querying Images by Content using Color, Texture, and Shape,” In Proc. of SPIE Electronic Imaging: Science and Technology, 1993.
[20] J. S Payne, T. J. Stonbam, “Can Texture and Image Content Retrieval Methods Match Human Perception,” In Proc. of Intelligent Multimedia, Video and Speech Processing, pp.154-157, 2001.
[21] A. Pentland, R.W. Picard, and S. Sclaroff, “Photobook : Tools for Content-Based Manipulation of image databases, ” International Journal of Computer Vision , Vol.18, pp. 233-254, 1996.
[22] K. Porkaew , S. Mehrotra, and M. Ortega, “Query Reformulation for Content Based Multimedia Similarity Retrieval in Mars,” In Proc. of IEEE Conference on Multimedia Computing and Systems,pp.747-751, 1999.
[23] Y. Rui, T. Huang, and S. Mehrotra, “Content-Based Image Retrieval with Relevance Feedback in MARS,” In Proc. of IEEE International Conference on Image Processing , pp. 815-818, 1997.
[24] T. Sikora, “The MPEG-7 Visual Standard for Content Description-An Overview, “ IEEE Transactions on Circuits Systems for Video Technology, Vol. 11, No. 6, 2001.
[25] J. R. Smith and S. F. Chang, “Visualseek: A Fully Automated Content-Based Image Query System,” In Proc. of the ACM International Multimedia Conference, pp.87-98, 1996.
[26] J. R. Smith, Integrated Spatial and Feature Image Systems: Retrieval, Compression and Analysis, PhD Thesis, Graduate School of Arts and Sciences, Columbia University, 1997.
[27] M. Sozio and A. Gionis, “The Community-Search Problem and How To Plan A Successful Cocktail Party,” In Proc. of 16th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, KDD ’10, pp.939-948, 2010.
[28] P. Y. Yin , S. H. Li, “Content-Based Image Retrieval Using Association rule Mining with Soft Relevance Feedback” Journal of Visual Communication and Image Representation, Vol.17, No. 5, pp.1108-1125, 2006.
[29] Y. Zhang, M.A. Nascimento, and O.R. Zaiane, “Building Image Mosaics: An Application of Content-Based Image Retrieval,” In Proc. of IEEE International Conference on Multimedia and Exposition, 2003.
[30] “MPEG-7 Visual Experimentation Model (XM) Version 10,” ISO/IEC/JTC1/SC29/WG11, Doc. N4063, 2001.
[31] “Overview of the MPEG-7 Standard Version 5.0,” Final Committee Draft, ISO/IECJTC1/SC29/WG11, Doc. N4031, 2001.
描述 碩士
國立政治大學
資訊科學學系
98971005
100
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0989710051
資料類型 thesis
dc.contributor.advisor 沈錳坤zh_TW
dc.contributor.advisor Shan, Man Kwanen_US
dc.contributor.author (Authors) 張瑋鈴zh_TW
dc.contributor.author (Authors) Chang, Wei Lingen_US
dc.creator (作者) 張瑋鈴zh_TW
dc.creator (作者) Chang, Wei Lingen_US
dc.date (日期) 2011en_US
dc.date.accessioned 30-Oct-2012 11:28:22 (UTC+8)-
dc.date.available 30-Oct-2012 11:28:22 (UTC+8)-
dc.date.issued (上傳時間) 30-Oct-2012 11:28:22 (UTC+8)-
dc.identifier (Other Identifiers) G0989710051en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54653-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 98971005zh_TW
dc.description (描述) 100zh_TW
dc.description.abstract (摘要) 近年來，隨著數位科技快速的發展，影像資料量迅速的增加，因此影像檢索成為重要的多媒體技術之一。在傳統的影像內容檢索技術中，使用影像低階特徵值，例如顏色(Color)、紋理(Texture)、形狀(Shape)等來描述影像的內容並進行圖片相似度的比對。然而，傳統的影像內容檢索僅提供單張影像查詢，很少研究多張影像的查詢。因此，本研究提出一個可針對多張影像查詢的方法以提供多張影像查詢的影像內容檢索。本研究將影像內容檢索結合社群網絡演算法，使用MPEG-7中相關特徵描述子和SIFT做為主要特徵向量，擷取影像的低階影像特徵，透過特徵相似度計算建立影像之間的網絡，並利用社群網絡演算法找出與多張查詢影像相似的影像。實驗結果顯示所提出的方法可精確的擷取到相似的影像。zh_TW
dc.description.abstract (摘要) In recent years, with the faster and faster development of computer technology, the number of digital images is grown rapidly so that the Content-Based Image Retrieval has become one of important multimedia technologies. Much research has been done on Content-Based Image Retrieval. However, little research has been done on query by multiple images. This thesis investigates the mechanism for query by multiple images.
First, MPEG-7 image features and SIFT are extracted from images. Then, we calculate the similarity of images to construct the proximity graph which represents the similarity structure between images. Last, processing of query by multiple images is achieved based on the social network algorithms. Experimental results indicate the proposed method provides high accuracy and precision.		en_US
dc.description.tableofcontents 摘要 I
ABSTRACT II
第一章 前言 1
1.1研究背景與動機 1
1.2 研究方法 3
1.3 研究貢獻 4
1.4論文架構 4
第二章 相關研究 5
2.1 MPEG-7 5
2.2影像低階特徵值 6
2.3影像內容檢索(Content-Based Image Retrieval)系統簡介 8
2.3.1 QBIC 8
2.3.2 VisualSEEK 9
2.3.3 VIR Image Engine 9
2.3.4 Blobworld 10
2.3.5 MARS（Multimedia Analysis and Retrieval System） 10
第三章 研究方法 14
3.1方法架構 14
3.2 影像特徵擷取(Image Feature Extraction) 15
3.2.1 Color Layout Descriptor (CLD) 16
3.2.2 Scalable Color Descriptor (SCD) 16
3.2.3 Color Structure Descriptor (CSD) 17
3.2.4 Dominant Color Descriptor (DCD) 18
3.2.5 Homogeneous Texture Descriptor (HTD) 19
3.2.6 Edge Histogram Descriptor (EHD) 20
3.2.7 Region Shape Descriptor (RSD) 21
3.2.8 Scale-Invariant Feature Transform (SIFT) 21
3.3 相似度正規化 24
3.4 建立Image Proximity Network 25
3.5 Community Search Algorithms 26
3.5.1 Maximizing the Minimum Degree 27
3.5.2 Communities with Size Restriction 28
第四章 實驗 35
4.1實驗資料 35
4.2實驗設計 36
4.3實驗結果 38
4.4實驗總結 44
第五章 結論與未來研究方向 45
5.1 結論 45
5.2 未來研究方向 45
參考文獻 47		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0989710051en_US
dc.subject (關鍵詞) 影像內容檢索zh_TW
dc.subject (關鍵詞) 多張影像查詢zh_TW
dc.subject (關鍵詞) 社群網絡zh_TW
dc.subject (關鍵詞) Content-Based Image Retrievalen_US
dc.subject (關鍵詞) Multiple Images Searchen_US
dc.subject (關鍵詞) Social Networken_US
dc.title (題名) 影像內容檢索中以社群網絡演算法為基礎之多張影像搜尋zh_TW
dc.title (題名) Query by Multiple Images for Content-Based Image Retrieval Based on Social Network Algorithmsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1] M. Bober, “MPEG-7 Visual Shape Descriptors,” IEEE Transactions on Circuits and System for Video Technology, Vol. 11, No. 6, pp. 716-719, 2001.
[2] C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Blobworld: Image Segmentation using Expectation-Maximization and Its Application to Image Querying,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 8, pp. 1026–1038, 2002.
[3] S. F. Chang, T. Sikora, and A. Puri, “Overview of MPEG-7 Standard,” IEEE Transactions on Circuits Systems for Video Technology, Vol. 11, No. 6, 2001.
[4] M. Flickner, H. Sawhney, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D.Lee, D. Petkovic, D. Steele, and P. Yanker, “Query By Image and Video Content : The QBIC System,” IEEE Computer Magazine, Vol. 28, No. 9, pp. 23–32, 1995.
[5] A. Gupta, “Visual Information Retrieval: A Virage Perspective,” Virage, Inc. , San Mateo, Calif., 1995.
[6] J. Han, and K. K. Ma, “Fuzzy Color Histogram and Its Use in Color Image Retrieval,” IEEE Transactions on Image Processing, Vol. 11, No. 8, pp. 944-952, 2002.
[7] R. Hess, “An Open-Source SIFT Library,” In Proc. of the ACM International Conference on Multimedia, pp.25–29, 2010.
[8] T. S. Huang, S. Mehrotra, and K. Ramachandran, “Multimedia Analysis and Retrieval System (MARS) Project,” In Proc. of 33rd Annual Clinic on Library Application of Data Processing-Digital Image Access and Retrieval, 1996.
[9] E. Kasutani, A. Yamada, “The MPEG-7 Color Layout Descriptor: a Compact Image Feature Description for High-speed Image/Video Segment Retrieval,” In Proc. of International Conference on Image Processing, pp. 674-677, 2001.
[10] H. K. Kim, J. D. Kim, D. G. Sim, and D. I. Oh, “A Modified Zernike Moment Shape Descriptor Invariant to Translation, Rotation and Scale for Similarity-Based Image Retrieval,” In Proc. of the IEEE International Conference on Multimedia and Expo, pp. 307-310, 2000.
[11] J. J. Koenderink, “The Structure of Images,” Biological Cybernetics, Vol. 50, No.
5, pp. 363-396, 1984.
[12] H. J. Lin, Y. T. Kao, S. H. Yen, and C. J. Wang, “A Study of Shape-Based Image Retrieval,” In Proc. of 24th International Conference on Distributed Computing Systems Workshops, pp. 118-123, 2004.
[13] T. Lindeberg, “Scale-space Theory: A Basic Tool for Analyzing Structures at Different Scales,” Journal of Applied Statistics, Vol. 21, No. 2, pp. 224-270, 1994.
[14] D. Lowe, “Distinctive Image Features from Scale-invariant Keypoints,” International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004.
[15] B. S Manjunath, G. M. Haley, and D. F. Dunn, “Efficient Gabor Filter Design for Texture Segmentation,” Pattern Recognition, Vol. 29, No. 12, pp. 2005-2016, 1996.
[16] B. S. Manjunath, J.-R. Ohm, V. V. Vasudevan, and A. Yamada, “MPEG-7 Color and Texture Descriptors,” IEEE Transactions on Circuit and System for Video Technoloy, Vol. 11, No. 6, pp. 703-715, 2001.
[17] B. S. Manjunath, P. Salembier, and T. Sikora,” Introduction to MPEG-7: Multimedia Content Description Standard,” New York: Wiley, 2001.
[18] J. M. Martinez, “Standards-MPEG-7 Overview of MPEG-7 Description tools, Part 2,” IEEE Multimedia, Vol. 9, No. 3, pp. 83-93, 2002.
[19] W. Niblack, R. Barber, W. Equitz, et al , “The QBIC Project: Querying Images by Content using Color, Texture, and Shape,” In Proc. of SPIE Electronic Imaging: Science and Technology, 1993.
[20] J. S Payne, T. J. Stonbam, “Can Texture and Image Content Retrieval Methods Match Human Perception,” In Proc. of Intelligent Multimedia, Video and Speech Processing, pp.154-157, 2001.
[21] A. Pentland, R.W. Picard, and S. Sclaroff, “Photobook : Tools for Content-Based Manipulation of image databases, ” International Journal of Computer Vision , Vol.18, pp. 233-254, 1996.
[22] K. Porkaew , S. Mehrotra, and M. Ortega, “Query Reformulation for Content Based Multimedia Similarity Retrieval in Mars,” In Proc. of IEEE Conference on Multimedia Computing and Systems,pp.747-751, 1999.
[23] Y. Rui, T. Huang, and S. Mehrotra, “Content-Based Image Retrieval with Relevance Feedback in MARS,” In Proc. of IEEE International Conference on Image Processing , pp. 815-818, 1997.
[24] T. Sikora, “The MPEG-7 Visual Standard for Content Description-An Overview, “ IEEE Transactions on Circuits Systems for Video Technology, Vol. 11, No. 6, 2001.
[25] J. R. Smith and S. F. Chang, “Visualseek: A Fully Automated Content-Based Image Query System,” In Proc. of the ACM International Multimedia Conference, pp.87-98, 1996.
[26] J. R. Smith, Integrated Spatial and Feature Image Systems: Retrieval, Compression and Analysis, PhD Thesis, Graduate School of Arts and Sciences, Columbia University, 1997.
[27] M. Sozio and A. Gionis, “The Community-Search Problem and How To Plan A Successful Cocktail Party,” In Proc. of 16th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, KDD ’10, pp.939-948, 2010.
[28] P. Y. Yin , S. H. Li, “Content-Based Image Retrieval Using Association rule Mining with Soft Relevance Feedback” Journal of Visual Communication and Image Representation, Vol.17, No. 5, pp.1108-1125, 2006.
[29] Y. Zhang, M.A. Nascimento, and O.R. Zaiane, “Building Image Mosaics: An Application of Content-Based Image Retrieval,” In Proc. of IEEE International Conference on Multimedia and Exposition, 2003.
[30] “MPEG-7 Visual Experimentation Model (XM) Version 10,” ISO/IEC/JTC1/SC29/WG11, Doc. N4063, 2001.
[31] “Overview of the MPEG-7 Standard Version 5.0,” Final Committee Draft, ISO/IECJTC1/SC29/WG11, Doc. N4031, 2001.		zh_TW