臺灣博碩士論文加值系統

傳統的影像內容檢索方法大多以計算使用者查詢圖片與系統中所有圖片之間的相似度為基礎，此稱為「範例式查詢系統」(Query-By-Example System)。然而，在現今既存的查詢回饋方法中，使用短期限的查詢回饋是很難改進圖片搜尋準確度。因此，這引發了我們的動機去發展一個重新定義的查詢圖片的技術 (Query Refinement Technique)，稱其NQ3。NQ3方法結合了瀏覽樣式(Navigation Patterns) 與混合型搜尋(Hybrid Search)的方法。混合型搜尋方法，我們稱之Q3， Q3代表的是「移動查詢中心點」(Query-Point-Movement, QPM)，「查詢特徵權重值調整」(Query-Reweighting, QR )，與「擴充查詢」(Query-Expansion, QEX) 三種方法的結合。普遍說來我們的方法與現今其他的方法最主要的不同在於我們的方法可以達到較高的準確率，且同時將視覺化多樣性、搜尋收斂、冗餘瀏覽的問題考量進去。因此，NQ3 可以搜尋有效性與效率地來進行互動式影像內容擷取。就效率性而言，藉由探勘使用者瀏覽圖片樣式的規則輔助，避免冗長的瀏覽回饋的次數；就有效性而言，藉由使用三種查詢關聯性回饋技術去克服圖片視覺上特徵值差異性的問題；就準確性跟回饋次數來看，我們的實驗結果證實我們的方法優於目前存在的方法。

Conventional approaches for content-based image retrieval are always on the basis of the computations of the similarities between user’s query and images via a query-by-example system. Indeed, no matter how the search strategies are powerful, it is very hard to make a precise search within a very short term of query feedbacks. Hence, it motivated us to develop an innovative query refinement technique, namely NQ3, that combines navigation patterns and the hybrid search strategy, namely Q3, with respect to QPM (Query-Point-Movement), QR (Query-Reweighting) and QEX (Query-EXpansion). Generally, the primary difference between our proposed approach and the other contemporary approaches is that we have accomplished excellent image retrieval with considering the problems for visual diversity, exploration convergence, redundant browse. In other words, the major contribution of this paper is that we propose a new Query-Refinement technique to help user make an effective and efficient exploration of images. For efficiency, the expected query formulation scenario is that the intolerant navigations are expected to be prevented by exploiting the navigation patterns hidden in user behaviors. For effect, three query refinement strategies will cooperate to overcome the obstacle about feature diversity. The experimental results reveal that our proposed approach is very effective for query refinement in terms of accuracy through the integration of navigation patterns and Q3.

中文摘要 I
英文摘要 III
誌謝 V
目錄 VI
表目錄 IX
圖目錄 X
第一章 導論 1
1.1　研究背景 1
1.2　研究動機與目的 3
1.3 研究方法概述 5
1.4 研究方法貢獻 7
1.5 論文架構 7
第二章　文獻探討 8
2.1　影像低階特徵植 8
2.2　內涵式影像搜尋系統(CBIR) 9
2.3　關聯性回饋查詢技術 10
2.3.1　查詢特徵權重值調整(Query Re-weighting) 11
2.3.2　移動查詢中心點(Query Point Movement) 11
2.3.3　擴充查詢(Query Expansion) 13
2.3.4　混合型(Hybrid)的關聯性回饋 13
2.4　關聯式規則探勘(Association Rule Mining) 14
2.4.1　關聯規則定義 14
2.4.2　關聯規則探勘方法 15
2.4.3　Apriori演算法 16
2.5　資料分群演算法(Data Clustering) 18
2.5.1　K-Means 18
第三章　研究方法 19
3.1　方法架構 19
3.2　樣式挖掘階段(Pattern Discovering Phase) 21
3.2.1　資料轉換(Data Transformation) 21
3.2.2　瀏覽樣式探勘(Navigation Patterns Mining) 23
3.2.3　樣式索引(Pattern Indexing) 25
3.3　線上處理之Q3搜尋步驟(Q3 Search Phase) 27
3.3.1　基本概念(Basic Idea) 27
3.3.2　Q3 搜尋演算法(Algorithm Q3 Search) 29
第四章　實驗分析 35
4.1　實驗資料 35
4.2　實驗規劃 44
4.3　實驗結果 46
4.3.1　門檻值s效能分析實驗 46
4.3.2　分群數目cl效能分析實驗 47
4.3.3 NQ3與其他傳統方法之準確率比較實驗 48
4.3.4 各類圖片準確率分析實驗 55
4.3.5　圖片數量與準確率之分析實驗 56
4.3.6　回傳數目準確率分析實驗 56
4.3.7　涵蓋率分析實驗 59
4.4 實驗總結 61
第五章　結論與未來研究方向 62
5.1　結論 62
5.2　未來研究方向 63
第六章　參考文獻 64
自述 70

[1] R. Agrawal, T. Imielinkski and A. Swami, “Mining Association Rules between Sets of Items in Large Databases.” In Proc. of ACM SIGMOD, Washington, D.C, pp. 207-216, May 1993.
[2] R. Agrawal and R. Srikant, “ Fast Algorithms for Mining Association Rules.” In Proc. of 20th Very Large Databases Conference, Santiago, pp. 487-499，1994.
[3] T. Ashwin, R. Gupta, and S. Ghosal, “Adaptable similarity search using non-relevant information.” In Proc. of the 28th VLDB Conference. Hong Kong, China, August 2002.
[4] N. Belkin, C. Cool, W. Croft, and J. Callan, “The effect of multiple query representations on information retrieval system performance. In Proc. of ACM SIGIR, Pittsburgh, PA, pp. 339–346,1993
[5] J. A. Black J., G. Fahmy, S. Panchanathan , “A Method for Evaluating the Performance of Content-Based Image Retrieval Systems.” In Proc. of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 96-100, 2002.
[6] A. Bouajila, C. Claus, V. Draga, A. Herkersdorf, W. Kohtz, D. Llorente, M. Meitinger, F. Muller, R. Ohlendorf, H. Rauchfuss, I. Ruge, G. Spohrle, W. Stechele, T. Wild, D. Zeller, and J. Zeppenfeld, MPEG-7 eXperimentation Model(XM), Software available
[7] R. Brunelli and O. Mich, “Image retrieval by examples.” IEEE Transactions on Multimedia, 2(3):164–171, September 2000.
[8] K. Chakrabarti and S. Mehrotra, “The hybrid tree: An index structure for high dimensional feature spaces.” In Proc. of 5th Int’l Conference on Data Engineering, pp. 440–447, Sydney, Australia, 1999
[9] K. Chakrabarti, K. Porkaew, and S. Mehrotra. “Efficient query refinement in multimedia databases.” In Proc. of the IEEE Int’l Conference on Data Engineering, pp.196. San Diego, USA, Feburary 2000.
[10] C. Djeraba, “Association and Content-Based Retrieval.” IEEE Transaction. Knowl. Data Eng. pp.118- 135 , Feburary 2003.
[11] 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, 28(9):23–32, September 1995.
[12] J.C. French, J.V.S. Watson, X. Jin, and W.N. Martin, “Integrating multiple multi-channel cbir systems.” In Proc. of International Work Shop on Multimedia Information System, pp.85-96 Ischia, Italy, May 2003.
[13] J.C. French, J.V.S. Watson, X. Jin, and W.N. Martin, “Using multiple image representations to improve the quality of content-based image retrieval.” Technical Report CS-2003-10, Dept. of Computer Science, University of Virgina , March, 2003.
[14] J. French, Xiangyu Jin, “An empirical investigation of the scalability of a multiple viewpoint CBIR system.” In Proc. of the 3rd International Conference of Image and Video Retrieval (CIVR04) ,pp.252-260,. Dublin, Ireland, July 2004.
[15] A. Gupta, “Visual Information Retrieval: A Virage Perspective.” 1995. http://www.virage.com/wpaper/
[16] J .Han, KK Ma, “Fuzzy color histogram and its use in color image retrieval.” IEEE transactions on Image Processing, Vol. 11, No. 8, pp 944-952, 2002.
[17] Jiawei Han and Micheline Kamber, “Data Mining: Concepts and Techniques.” Morgan Kaufmann Publisher, 1999.
[18] J. A. Hartigan, M.A. Wong , “A k-means clustering algorithm.” Applied Statistics, Vol. 28, pp. 100-108, 1979.
[19] K. Hirata and T. Kato, “Query by visual example-content based image retrieval. In Proc. of the 3rd International Conference on Extending Database Technology, Vol. 580 of LNCS, pp. 56–71,Vienna, Austria, March 1992
[20] Y. Ishikawa, R. Subramanya, and C. Faloutsos , “Mindreader: Querying databases through multiple examples.” In Proc. of the 24th VLDB Conference, pp. 218–227. New York, USA, 1998.
[21] A. Jain and D. Zongker, “Feature Selection : Evaluation, Application, and Small Sample Performance.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, Issue 2, pp. 153-158, February 1997.
[22] X. Jin and J.C. French, “Improving image retrieval effectiveness via multiple queries.” Multimedia Tools and Application ,Vol. 26, pp. 221–245, June 2005
[23] L. Kaufman and P. J. Rousseeuw, “Finding Groups in Data: an Introduction to Cluster Analysis.”John Wiley & Sons, 1990.
[24] D-.H. Kim and C.-W. Chung, “Qcluster : relevance feedback using adaptive clustering for content-based image retrieval.” In Proc. of the ACM SIGMOD International Conference on Management of Data, pp. 599--610, San Diego, California, 2003.
[25] Hae-Kwang Kim, Jong-Deuk Kim, Dong-Gyu Sim, Dae-Il 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(ICME),Vol. 1, pp. 307-310).,New York ,2000.
[26] 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, March 2004.
[27] 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, San Jose, CA, 1993.
[28] 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, Hong Kong, China , 2001.
[29] 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.
[30] K. Porkaew and K. Chakrabarti, and S. Mehrotra, “Query refinement for multimedia similarity retrieval in MARS.” In Proc. of the 7th ACM Multimedia Conference, pp. 235–238. Orlando, Florida, 1999.
[31] 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, P.747-751,1999
[32] J. J. Rocchio, “Relevance feedback in information retrieval.” In Gerard Salton, editor, The SMART Retrieval System – Experiement in Automatic DOcument Processing, pages 313–323, 1971.
[33] 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, New York 1997.
[34] Y. Rui, T. Huang, M. Ortega, and S. Mehrotra. “Relevance feedback: A power tool for interactive content-based image retrieval.” IEEE Transactions on Circuits and Systems for Video Technology, vol .8(5), pp.644–655, 1998.
[35] Gerard Salton, Chris Buckley, “Improving retrieval performance by relevance feedback.” Journal of the American Society for Information Science and Technology(JASIS) ,Vol. 41, No.4, pp.288-297,1990.
[36] A. W. M. Smeulders, Marcel Worring , Simone Santini, Amarnath Gupta,Ramesh Jain, “Content-based image retrieval at the end of the early years”, IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI), vol. 22(12),pp.1349-1379,2000
[37] J. R. Smith and S. F. Chang, “Visualseek: A fully automated content-based image query system.” In Proc. of the ACM Int’l Multimedia Conference, pp.87–98, November 1996
[38] K. Vu, Kien A. Hua, Ning Jiang, “Improving Image Retrieval Effectiveness in Query-by-Example Environment.” In Proc. of the 2003 ACM symposium on Applied computing, pp .774-781, 2003.
[39] L. Wu, C. Faloutsos, K. Sycara, and T. R. Payne, “FALCON: Feedback adaptive loop for content-based retrieval.” In Proc. of the 26th VLDB Conference, pp. 297–306, Cairo, Egypt, 2000.
[40] Peng-Yeng Yin, Bir Bhanu, Kuang-Cheng Chang, Anlei Dong, “Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning.” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol .27(10), pp.1536-1551 , 2005.,
[41] Peng-Yeng Yin, Shin-Huei Li, “Content-based image retrieval using association rule mining with soft relevance feedback.” Journal of Visual Communication and Image Representation, vol.17(5), pp.1108-1125 ,2006.
[42] H. You, E. Chang, and B. Li, “NNEW: Nearest neighbor expansion by weighting in image database retrieval.” In Proc. of the second International Conference on Multimedia and Exposition (ICME),pp. 178- 181 , August, 2001 http://wwwdb.stanford.edu/echang/nnew.ps
[43] H. Zhang, Z. Chen, M. Li, and Z. Su, “Relevance feedback and learning in content-based image search.” World Wide Web, Vol. 6, No. 2, pp. 131–155, 2003.
[44] Y. Zhang, M.A. Nascimento, and O.R. Zaiane, “Building Image Mosaics: An Application of Content-based Image Retrieval.” IEEE International Conference on Multimedia and Exposition, Baltimore, MD, USA, July 6-9, 2003.
[45] http://www.corel.com.tw/