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題名 美術影像中顏色風格探勘之研究
Mining Painting Color Style from Fine Arts
作者 劉勁男
Chin-Nan Liu
貢獻者 沈錳坤
Man-Kwan Shan
劉勁男
Chin-Nan Liu
關鍵詞 資料探勘
美術影像風格
影像擷取
Data Mining
Painting Style
Content-based image retrieval
日期 2002
上傳時間 17-Sep-2009 13:52:04 (UTC+8)
摘要 資料探勘技術的研究，隨著資料庫系統的普遍建置而日益重要。但是尚沒有研究針對美術繪畫影像的風格探勘。本研究的目的也就是發展資料探勘的技術，從繪畫的低階影像特徵中探勘出繪畫風格，並以分類規則的方式來表示繪畫風格。畫家的畫風是指表現在大部分畫作裡的繪畫風格，也是與其他畫家相比，在畫作的共同特徵上之獨特性與差異性。基於以上的兩個特性，我們把畫風探勘分為三個議題︰一、feature extraction，從美術影像中萃取低階影像特徵，我們使用的有主要顏色與相鄰顏色。為了因應MPEG-7標準即將統一描述多媒體資料的內容表示方式，所以我們也針對MPEG-7規格的低階影像特徵。二、mining frequent patterns，從所有該畫家畫作的低階影像特徵找出共同的個人畫作特徵，我們利用association rule中mining frequent itemset的方法找出畫風中顏色的搭配，而且我們也發展了一個新的規則，frequent 2D sequential pattern，用來表示畫風中顏色的佈局。三、classification，找出每個畫家與別人不一樣的個人畫作特徵，就是定量描述的繪畫影像風格。我們分別利用C4.5與修改過的associative classification。我們提出了二個改進associative classification的分類演算法，single-feature variant support (SFVS) classification，容許各個class進行不同minimum support的mining以及與multi-feature variant support (MFVS) classification，同時用不同低階影像特徵進行分類。有關實驗的進行，我們有兩組測試畫家，一組是西方印象派畫家，另一組則是受西方印象派影響的臺灣本土畫家。每組畫家都進行兩人配對，分別建出2-way的associative classifier、SFVS classifier與MFVS classifier，並評估畫風探勘演算法的效果。最後，本論文實作了一個「影像風格查詢系統」。查詢系統的基本功能提供使用者以風格查詢藝術影像的功能。例如，使用者可以查詢具有梵谷畫風的畫作或是查詢融合雷諾瓦與莫內畫風的畫作。
The data mining researches become more and more important. However, no studies have investigated on painting style mining of fine arts images. The purpose of this paper is to develop a new approach for mining painting style from low level image features of fine art images and represent painting style as the classification rules. The painitng style of an artist is characterized not only by the frequent pattern appears in most works but also by the discrimination patterns from others. According to these two characteristics, we identified three design issues for painting style mining: feature extraction, mining frequent patterns and classification. Feature extraction extracts low level image freatures from fine arts images. In this thesis, we extract dominant color and adjacency color relationship as low level image features. Besides, we also extract MPEG-7 descriptors. Mining frequent patterns finds the frequent patterns appear in all works by one artist. We apply the technique of frequent itemset mining in association rule mining to find which colors are likely be used together in artist’s painting style. Moreover, we proposed a new pattern, frequent 2D subsequence, to represent painting style in terms of color layout. Classification finds the artist’s discriminating patterns from others and presents those patterns as painting style in quantitative manner. We utilize C4.5 and modified associative classification as classification methods. We developed two association classification algorithm, single-feature variant support (SFVS) classification and multi-feature variant support (MFVS) classification. The experiment is conducted by two groups of painting work. One is the work of impressionism artists and the other is the work of Taiwan artists that were influenced by impressionism. The 2-way associative classifier, SFVS classifier and MFVS classifier are constructed for each group of painting work and evaluate the proformance. Finally, we implemented a “Painting Style Query System” which provides users to query fine arts images by painting style. For example, user can query those images that matchs VanGogh’s style or query those images that matchs integration style with Renoir and Monet.
參考文獻 [1] R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules, in Proceedings of International Conference on Very Large Data Bases, 1994.
[2] R. Agrawal and R. Srikant, Mining Sequential Patterns, in Proceedings of International Conference on Data Engineering, 1995.
[3] K. Alsabti, S. Ranka and V. Singh, CLOUDS: A Decision Tree Classifer for Large Datasets, in Proceedings of Fourth International Conference on Knowledge Discovery and Data Mining, 1998.
[4] L. Breiman, Bagging Predictors, Machine Learning, Vol. 24, No. 2, 1996.
[5] 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.
[6] S. K. Chang, Q. Y. Shi and C. W. Yang, Iconic Indexing by 2D Strings, IEEE Transactions Pattern Analysis and Machine Intelligence, Vol. 9, No. 3, 1987.
[7] M. S. Chen, J. Han and P. S. Yu, Data Mining: An Overview from a Database Perspective, IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, 1996.
[8] M. C. Chuang and L.C. Ou, Influence of a Holistic Color Interval on Color Harmony, Color, Research and Application, Vol. 26, No. 1, 2001.
[9] C. Colombo, A. D. Bimbo and P. Pala, Semantics in Visual Information Retrieval, IEEE Multimedia, Vol. 6, No. 3, 1999.
[10] J. M. Corridoni, A. D. Bimbo and P. Pala, Image Retrieval by Color Semantics, ACM Multimedia Systems, Vol. 7, No. 3, 1999.
[11] A. Czyzewski, Mining Knowledge in Noise Audio Data, in Proceedings of Second International Conference on Knowledge Discovery and Data Mining, 1996.
[12] Y. Deng and B. S. Manjunath, Unsupervised Segmentation of Color-Texture Regions in Images and Video, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 8, 2001.
[13] F. Ennesser and G. Medioni, Finding Waldo, or Focus of Attention using Local Color Information, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 8, 1995.
[14] C. Faloutsos et al. Efficient and Effective Query by Image Content, Journal of Intelligent Information Systems, Vol. 3, No. 3, 1994.
[15] M. Flickner et al., Query by Image and Video Content: the QBIC System, IEEE Computer, Vol. 28, No. 9, 1995.
[16] J. E. Gehrke, V. Ganti, R. Ramakrishnan and W. Y. Loh, BOAT: Optimistic Decision Tree Construction, in Proceedings of the 1999 SIGMOD Conference, 1999.
[17] A. Gersho and R. M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Publishers, Norwell, Mass., 1993.
[18] J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2001.
[19] J. Han and Y. Fu, Discovery of Multiple-Level Association Rules from Large Databases, in Proceedings of 1995 International Conference on Very Large Data Bases, 1995.
[20] J. Han, and Y. Fu, Discovery of Multiple-Level Association Rules from Large Databases, IEEE Transactions on Knowledge and Data Engineering, Vol. 11, No. 5, 1999.
[21] A. K. Jain and A. Vailaya, Image Retrieval using Color and Shape, Pattern Recognition, Vol. 29, No. 8, 1996.
[22] C. Kenney, Y. Deng, B. S. Manjunath and G. Hewer, Peer Group Image Enhancement, IEEE Transactions on Image Processing, Vol. 10, No. 2, 2001.
[23] M. Kuntz, Mining Multimedia Data: New Problems and Interaction-based Solutions, in Proceedings of 9th ERCIM Database Research Group Workshop on Multi Database Systems (EDGR9), 1996.
[24] B. Liu, W. Hsu, and Y. Ma, Integrating Classification and Association Rule Mining, in Proceedings of ACM International Conference on Knowledge Discovery and Data Mining KDD, 1998.
[25] C. C. Liu, J. L. Hsu and A. L. P. Chen, Efficient Theme and Non-Trivial Repeating Pattern Discovering in Music databases, in Proceedings of IEEE International Conference on Data Engineering, 1999.
[26] B. S. Manjunath, J. Ohm, V. V. Vasudevan, and A. Yamada, Color and Texture Descriptors, IEEE Transactions on Circuits Systems for Video Technology, Vol. 11, No. 6, 2001.
[27] Overview of the MPEG-7 Standard, Version 5.0, Final Committee Draft, ISO/IEC JTC1/SC29/WG11, Doc. N4031, 2001.
[28] MPEG-7 Visual Experimentation Model (XM), Version 10.0, ISO/IEC/JTC1/SC29/WG11, Doc. N4063, 2001.
[29] J. R. Quinlan, C4.5: Programs for Machine Learning, San Mateo, CA: Morgan Kaufmann, 1993.
[30] R. Sablatnig, P. Kammerer, and E. Zolda, Structural Analysis of Paintings based on Brush Strokes, in Proceedings of SPIE Scientific Detection of Fakery in Art, SPIE-Vol. 3315, 1998.
[31] J. R. Smith and S. F. Chang, VisualSeek: A Fully Automated Content-Based Image Query System, in Proceedings of ACM Multimedia`96, 1996.
[32] 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.
[33] J. R. Smith and S. F. Chang, VisualSeek: A Fully Automated Content-Based Image Query System, in Proceedings of ACM Multimedia`96, 1996.
[34] R. Srikant and R. Agrawal, Mining Generalized Association Rules, in Proceedings of the 21st International Conference on Very Large Databases, 1995.
[35] M. Swain and D. Ballard, Color Indexing, International Journal of Computer Vision, Vol. 7, No. 1, 1991.
[36] H. Tamura, S. Mori and T. Yamawaki, Texture Features Corresponding to Visual Perception, IEEE Transaction on System, Man, and Cybernetics, Vol. SMC-8, No. 6, 1978.
[37] P. N. Tan, H. Blau, S. Harp and R. Goldman, Textual Data Mining of Service Center Call Records, in Proceedings of ACM KDD`00, 2000.
[38] Text of ISO/IEC 15938-3 Multimedia Content Description Interface - Part 3: Visual. Final Committee Draft, ISO/IEC/JTC1/SC29/WG11, Doc. N4062, 2001.
[39] A. Vailaya, A. T. Figueriedo, A. K. Jain and H. J. Zhang, Image Classification for Content-Based Indexing, IEEE Transactions on Image Processing, Vol. 10, No. 1, 2001.
[40] J. Z. Wang, J. Li, and G. Wiederhold, SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 9, 2001.
[41] O. R. Zaiane, J. Han, Z. N. Li, J. Y. Chiang, and S. Chee, Multimedia-Miner: A System Prototype for Multimedia Data Mining, in Proceedings of 1998 ACM-SIGMOD Conference on Management of Data, (system demo), 1998.
[42] O. R. Zaïane, J. Han, Z. N. Li and J. Hou, Mining Multimedia Data, in Proceedings of CASCON`98: Meeting of Minds, 1998.
[43] 大山正，色彩心理學 : 追尋牛頓和歌德的腳步，牧村圖書出版，1998。
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描述 碩士
國立政治大學
資訊科學學系
89753010
91
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0089753010
資料類型 thesis
dc.contributor.advisor 沈錳坤zh_TW
dc.contributor.advisor Man-Kwan Shanen_US
dc.contributor.author (Authors) 劉勁男zh_TW
dc.contributor.author (Authors) Chin-Nan Liuen_US
dc.creator (作者) 劉勁男zh_TW
dc.creator (作者) Chin-Nan Liuen_US
dc.date (日期) 2002en_US
dc.date.accessioned 17-Sep-2009 13:52:04 (UTC+8)-
dc.date.available 17-Sep-2009 13:52:04 (UTC+8)-
dc.date.issued (上傳時間) 17-Sep-2009 13:52:04 (UTC+8)-
dc.identifier (Other Identifiers) G0089753010en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/32617-
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) 89753010zh_TW
dc.description (描述) 91zh_TW
dc.description.abstract (摘要) 資料探勘技術的研究，隨著資料庫系統的普遍建置而日益重要。但是尚沒有研究針對美術繪畫影像的風格探勘。本研究的目的也就是發展資料探勘的技術，從繪畫的低階影像特徵中探勘出繪畫風格，並以分類規則的方式來表示繪畫風格。畫家的畫風是指表現在大部分畫作裡的繪畫風格，也是與其他畫家相比，在畫作的共同特徵上之獨特性與差異性。基於以上的兩個特性，我們把畫風探勘分為三個議題︰一、feature extraction，從美術影像中萃取低階影像特徵，我們使用的有主要顏色與相鄰顏色。為了因應MPEG-7標準即將統一描述多媒體資料的內容表示方式，所以我們也針對MPEG-7規格的低階影像特徵。二、mining frequent patterns，從所有該畫家畫作的低階影像特徵找出共同的個人畫作特徵，我們利用association rule中mining frequent itemset的方法找出畫風中顏色的搭配，而且我們也發展了一個新的規則，frequent 2D sequential pattern，用來表示畫風中顏色的佈局。三、classification，找出每個畫家與別人不一樣的個人畫作特徵，就是定量描述的繪畫影像風格。我們分別利用C4.5與修改過的associative classification。我們提出了二個改進associative classification的分類演算法，single-feature variant support (SFVS) classification，容許各個class進行不同minimum support的mining以及與multi-feature variant support (MFVS) classification，同時用不同低階影像特徵進行分類。有關實驗的進行，我們有兩組測試畫家，一組是西方印象派畫家，另一組則是受西方印象派影響的臺灣本土畫家。每組畫家都進行兩人配對，分別建出2-way的associative classifier、SFVS classifier與MFVS classifier，並評估畫風探勘演算法的效果。最後，本論文實作了一個「影像風格查詢系統」。查詢系統的基本功能提供使用者以風格查詢藝術影像的功能。例如，使用者可以查詢具有梵谷畫風的畫作或是查詢融合雷諾瓦與莫內畫風的畫作。zh_TW
dc.description.abstract (摘要) The data mining researches become more and more important. However, no studies have investigated on painting style mining of fine arts images. The purpose of this paper is to develop a new approach for mining painting style from low level image features of fine art images and represent painting style as the classification rules. The painitng style of an artist is characterized not only by the frequent pattern appears in most works but also by the discrimination patterns from others. According to these two characteristics, we identified three design issues for painting style mining: feature extraction, mining frequent patterns and classification. Feature extraction extracts low level image freatures from fine arts images. In this thesis, we extract dominant color and adjacency color relationship as low level image features. Besides, we also extract MPEG-7 descriptors. Mining frequent patterns finds the frequent patterns appear in all works by one artist. We apply the technique of frequent itemset mining in association rule mining to find which colors are likely be used together in artist’s painting style. Moreover, we proposed a new pattern, frequent 2D subsequence, to represent painting style in terms of color layout. Classification finds the artist’s discriminating patterns from others and presents those patterns as painting style in quantitative manner. We utilize C4.5 and modified associative classification as classification methods. We developed two association classification algorithm, single-feature variant support (SFVS) classification and multi-feature variant support (MFVS) classification. The experiment is conducted by two groups of painting work. One is the work of impressionism artists and the other is the work of Taiwan artists that were influenced by impressionism. The 2-way associative classifier, SFVS classifier and MFVS classifier are constructed for each group of painting work and evaluate the proformance. Finally, we implemented a “Painting Style Query System” which provides users to query fine arts images by painting style. For example, user can query those images that matchs VanGogh’s style or query those images that matchs integration style with Renoir and Monet.en_US
dc.description.tableofcontents 第一章 導論 1
1.1 研究動機與目的 1
1.2 論文架構 5
第二章 畫風、影像特徵與資料探勘 6
2.1畫風 6
2.2影像特徵 6
2.2.1 Content-Based Image Retrieval 6
2.2.2 MPEG-7 9
2.2.2.1 Overview 10
2.2.2.2 Visual Part 11
2.3 資料探勘 17
2.3.1 Association Rules 18
2.3.2 Sequential Patterns 22
2.3.3分類演算法 25
2.3.3.1 C4.5 Classification 25
2.3.3.2 Associative Classification 25
第三章 畫風探勘 28
3.1影像特徵擷取(IMAGE FEATURE EXTRACTION) 28
3.2 頻繁樣式探勘(FREQUENT PATTERN MINING) 32
3.2.1 主要顏色與相鄰顏色特徵之探勘 32
3.2.2 MPEG-7 Descriptors之探勘 34
3.3 建構分類器(CLASSIFICATION) 39
3.3.1 Associative Classification 40
3.3.2 Variant Support分類器(SFVS) 42
3.3.3 Multiple-Feature Variant Support分類器(MFVS) 45
3.3.4 Bagging Predictors 46
第四章 實驗結果與討論 48
4.1實驗設計 48
4.2實驗一︰臺灣本土畫家畫風分類準確度 49
4.3 實驗二︰印象派畫家畫風分類準確度 58
4.4討論 64
第五章 應用 66
5.1系統功能 66
5.2系統架構 66
5.3系統流程 67
第六章 結論 69
參考文獻 70		zh_TW
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dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0089753010en_US
dc.subject (關鍵詞) 資料探勘zh_TW
dc.subject (關鍵詞) 美術影像風格zh_TW
dc.subject (關鍵詞) 影像擷取zh_TW
dc.subject (關鍵詞) Data Miningen_US
dc.subject (關鍵詞) Painting Styleen_US
dc.subject (關鍵詞) Content-based image retrievalen_US
dc.title (題名) 美術影像中顏色風格探勘之研究zh_TW
dc.title (題名) Mining Painting Color Style from Fine Artsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1] R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules, in Proceedings of International Conference on Very Large Data Bases, 1994.zh_TW
dc.relation.reference (參考文獻) [2] R. Agrawal and R. Srikant, Mining Sequential Patterns, in Proceedings of International Conference on Data Engineering, 1995.zh_TW
dc.relation.reference (參考文獻) [3] K. Alsabti, S. Ranka and V. Singh, CLOUDS: A Decision Tree Classifer for Large Datasets, in Proceedings of Fourth International Conference on Knowledge Discovery and Data Mining, 1998.zh_TW
dc.relation.reference (參考文獻) [4] L. Breiman, Bagging Predictors, Machine Learning, Vol. 24, No. 2, 1996.zh_TW
dc.relation.reference (參考文獻) [5] 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.zh_TW
dc.relation.reference (參考文獻) [6] S. K. Chang, Q. Y. Shi and C. W. Yang, Iconic Indexing by 2D Strings, IEEE Transactions Pattern Analysis and Machine Intelligence, Vol. 9, No. 3, 1987.zh_TW
dc.relation.reference (參考文獻) [7] M. S. Chen, J. Han and P. S. Yu, Data Mining: An Overview from a Database Perspective, IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, 1996.zh_TW
dc.relation.reference (參考文獻) [8] M. C. Chuang and L.C. Ou, Influence of a Holistic Color Interval on Color Harmony, Color, Research and Application, Vol. 26, No. 1, 2001.zh_TW
dc.relation.reference (參考文獻) [9] C. Colombo, A. D. Bimbo and P. Pala, Semantics in Visual Information Retrieval, IEEE Multimedia, Vol. 6, No. 3, 1999.zh_TW
dc.relation.reference (參考文獻) [10] J. M. Corridoni, A. D. Bimbo and P. Pala, Image Retrieval by Color Semantics, ACM Multimedia Systems, Vol. 7, No. 3, 1999.zh_TW
dc.relation.reference (參考文獻) [11] A. Czyzewski, Mining Knowledge in Noise Audio Data, in Proceedings of Second International Conference on Knowledge Discovery and Data Mining, 1996.zh_TW
dc.relation.reference (參考文獻) [12] Y. Deng and B. S. Manjunath, Unsupervised Segmentation of Color-Texture Regions in Images and Video, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 8, 2001.zh_TW
dc.relation.reference (參考文獻) [13] F. Ennesser and G. Medioni, Finding Waldo, or Focus of Attention using Local Color Information, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 8, 1995.zh_TW
dc.relation.reference (參考文獻) [14] C. Faloutsos et al. Efficient and Effective Query by Image Content, Journal of Intelligent Information Systems, Vol. 3, No. 3, 1994.zh_TW
dc.relation.reference (參考文獻) [15] M. Flickner et al., Query by Image and Video Content: the QBIC System, IEEE Computer, Vol. 28, No. 9, 1995.zh_TW
dc.relation.reference (參考文獻) [16] J. E. Gehrke, V. Ganti, R. Ramakrishnan and W. Y. Loh, BOAT: Optimistic Decision Tree Construction, in Proceedings of the 1999 SIGMOD Conference, 1999.zh_TW
dc.relation.reference (參考文獻) [17] A. Gersho and R. M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Publishers, Norwell, Mass., 1993.zh_TW
dc.relation.reference (參考文獻) [18] J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2001.zh_TW
dc.relation.reference (參考文獻) [19] J. Han and Y. Fu, Discovery of Multiple-Level Association Rules from Large Databases, in Proceedings of 1995 International Conference on Very Large Data Bases, 1995.zh_TW
dc.relation.reference (參考文獻) [20] J. Han, and Y. Fu, Discovery of Multiple-Level Association Rules from Large Databases, IEEE Transactions on Knowledge and Data Engineering, Vol. 11, No. 5, 1999.zh_TW
dc.relation.reference (參考文獻) [21] A. K. Jain and A. Vailaya, Image Retrieval using Color and Shape, Pattern Recognition, Vol. 29, No. 8, 1996.zh_TW
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