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姓名	周凡迪(Fan-Di Jou)  查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	直方統計圖之深入分析探討及其於影像檢索之應用 (Comprehensive Analysis of Histograand Its Application to Image Retrieval)
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摘要(中)	直方統計圖的比對是圖形識別的相關應用中常需使用到的技術，比對兩個不同的圖樣可藉由比對相對應的直方統計圖來完成。在本篇論文中，我們將深入分析直方統計圖的特質，並且將之應用在影像檢索技術的提升。我們所探討的主題依次是 一、直方統計圖的空間包容度的分析。在此我們提出了一個公平的比較方法，可以利用直方統計圖的空間包容度的量測來比較各種不同特徵的直方統計圖或不同量測函數的鑑別能力。 二、直方統計圖的模糊化技術。藉由模糊化技術可以克服由於直方統計圖統計圖的鑑別力提升卻造成相似圖樣的距離同時被拉大的副作用。 三、自動化的相似度判斷機制。可以取代傳統上以人工方式評估影像檢索系統效能的缺點，是一個客觀而且能夠量化統計分析影像檢索檢索效能的技術。 四、大型直方統計圖的比對演算法。我們提出了一個適用於大部分常見量測函數與直方統計圖的大小無關的比對演算法。藉由我們的技術，直方統計圖的鑑別能力可藉由提高特徵數或各個特徵的解析度自由提升，而不必擔心直方統計圖大小對效率的影響。 最後本論文以數個影像檢索的實驗分析驗證我們提出的方法，並做出總結。
摘要(英)	Histogram matching is a commonly adopted technique in the applications of pattern recognition. The matching of two patterns can thus be accomplished by matching their corresponding histograms. In this dissertation, we comprehensively analyze the characteristics of histogram and discuss the issues frequently appeared in image retrieval by utilizing the detail analyzing results. The characteristics inherent in histograms that we study are histogram capacity and histogram smoothing. Through the analysis of histogram capacities, researchers can evaluate and compare the discrimination capabilities of different histogram measurements or histograms embedded with different features. As to the histogram smoothing technique, it can compensate the penalty resulting from the enlargement of histogram dissimilarities of similar patterns due to the increase of discrimination capability. Moreover, an automatic similarity judgment mechanism without human involving for the evaluation of the retrieval effectiveness of image retrieval algorithms is proposed. Traditionally, researchers usually use the statistic scores of similarity judgments deriving from a few persons to demonstrate the performance of their image retrieval systems. However, the statistic scores of similarity judgment are too subjective. Through the use of our proposed similarity judgment mechanism, the retrieval effectiveness of image retrieval systems can be evaluated objectively, transparently and quantitatively. As we know, the number of features and the resolution of each feature will determine the size of histograms. Usually, the more the number of features and the higher the resolution of each feature, the stronger the discrimination capability of histograms will be. Unfortunately, it will lead to the decrease of the efficiency of histogram matching because traditional algorithms in evaluating similarity are all relevant to the histogram size. In this dissertation, a novel histogram-matching algorithm is proposed whose efficiency is irrelevant to the histogram size. By adopting our proposed algorithm, future researchers can focus more on the selection and combination of histogram features and freely adjust the resolution of each feature without worrying the decrease of retrieval efficiency. To demonstrate the feasibility and validity of the analyzing results and the proposed algorithms, a similar image retrieval experiment, which was constructed based on multi-feature histogram, is conducted. Although the experiment itself is the image retrieval application, the proposed histogram-relating techniques are not merely limited to be employed to image patterns. They can be applied to other retrieval-like applications which are independent of images or even other fields irrelevant to retrieval-like applications.
關鍵字(中)	★ 影像檢索 ★ 直方統計圖	關鍵字(英)	★  large-size histogram matching ★ similarity judgment ★ histogram smoothing ★ histogram capacity ★ image retrieval
論文目次	ABSTRACT i CONTENTS iii LIST OF FIGURES vi LIST OF TABLES viii LIST OF EQUATIONS ix LIST OF DEFINITIONS xiv LIST OF ALGORITHMS xv CHAPTER 1 INTRODUCTION 1 1.1 Motivation 1 1.2 Review of Relative Works 8 1.3 Organization of the Dissertation 12 CHAPTER 2 FOUNDATIONS OF HISTOGRAM 13 2.1 Definition and Notation 13 2.2 Histogram Similarity Measurements 18 CHAPTER 3 COMPREHENSIVE ANALYSIS OF HISTOGRAM CHARACTERISTICS 23 3.1 Histogram Capacity 24 3.1.1 The Capacity of Histogram Space 24 3.1.2 Fair Comparison between Dissimilarity Measurements 28 3.2 Histogram Smoothing 37 3.2.1 Histogram Smoothing 37 3.2.2 Quantized Average Smoothing Histogram 42 3.3 Concluding Remarks and Discussion 53 CHAPTER 4 QUANTITATIVE ANALYSIS FOR SIMILAR IMAGE RETRIEVAL 55 4.1 Motivation 55 4.2 Video Indexing 57 4.2.1 Introduction of Video Indexing Problem 57 4.2.2 Video Segmentation 60 4.2.3 Key Frame Extraction 63 4.3 Automatic Mechanism for Similarity Judgment 68 4.4 Concluding Remarks and Discussion 84 CHAPTER 5 EFFICIENT MATCHING FOR LARGE MULTI-FEATURE HISTOGRAM IN IMAGE RETRIEVAL 87 5.1 Multi-Feature Histogram 88 5.2 Efficient Histogram-matching Algorithm 91 5.2.1 Traditional Algorithm 91 5.2.2 Vinod’s Algorithm 94 5.2.3 Our Approaches 98 5.3 Concluding Remarks and Discussion 104 CHAPTER 6 EXPERIMENTAL RESULTS 107 6.1 Scenario 107 6.1.1 Thumbnail Images Scheme 108 6.1.1 Measurement Functions in Experiment 109 6.1.2 Experimental Histograms 110 6.2 Experimental Results 111 6.2.1 Comparison of Retrieval Effectiveness between Histograms with Different Histogram Sizes 112 6.2.2 Comparison of Retrieval Efficiency between Efficient Matching and Traditional Method 114 6.2.3 Comparison of Retrieval Effectiveness plus the Quantized Average Smoothing Histogram Method 116 6.2.4 Comparison of Retrieval Effectiveness between Images with Different Sizes 119 CHAPTER 7 CONCLUSIONS 121 7.1 Concluding Remarks 121 7.2 Future Works 125 REFERENCES 127 APPENDIX 141 Appendix A 141 Appendix B 143 Appendix C 144 Appendix D 146
參考文獻	[1] R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison Wesley, 2002. [2] J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles, Addison-Wesley, 1974. [3] C. H. Papadimitriou and K. Steiglitz, Combinatorial Optimization, Prentice Hall, Englewood, Cliffs, New Jersey, 1982. [4] W. Martin, Recent Theories of Narrative, chapter 5, Cornell University Press, Ithaca, NY, USA, first ed., 1986. [5] S. Ross, A First Course in Probability, page 229, Prentice Hall, fifth ed., 1998. [6] S. Theodoridis and K. Koutroumbas, Pattern Recognition, Academic Press, 1999. [7] R. P. Grimaldi, Discrete and Combinatorial Mathematics, Addison-Wesley, second ed., 1989. [8] S. H. Friedberg, A. J. Insel and L. E. Spence, Linear Algebra, Prentice Hall, second ed., 1989. [9] R. W. G. Hunt, Measuring Colour, Ellis Horwood, second ed., 1991. [10] G. Wyszecki and W. S. Stiles, Color Science – Concepts and Methods, Quantitative Data and Formulae, John Wiley and Sons, second ed., 1982. [11] R. Sedgewick, Algorithms, Addison-Wesley, second ed., 1988. [12] T. H. Cormen, C. E. Leiserson, and R. L. Rivest, Introduction to Algorithms, The MIT Press, 1994. [13] T. Kohonen, Self-Organizing Maps, Springer, Berlin, 1995. [14] J. Kapur and H. Kesavan, Entropy Optimization Principles with Applications, Academic Press, 1992. [15] H. Royden, Real Analysis, New York, MacMillan Publishing, 1968. [16] D. W. Jacobs, D. Weinshall, and Y. Gdalyahu, “Classification with Nonmetric Distances: Image Retrieval and Class Representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, 22(6): 583-600, 2000. [17] M. J. Swain and D. H. Ballard, “Color indexing,” Intern. Journal of Computer Vision 7(1): 11-32, 1991. [18] J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack, “Efficient Color Histogram Indexing for Quadratic Form Distance Functions,” IEEE Trans. Pattern Analysis and Machine Intelligence, 17(7): 729-736, 1995. [19] R. M. Ford, C. Robson, D. Temple, and M. Gerlach “Metrics for scene change detection in digital video sequences,” Multimedia Computing and Systems '97, Proceedings IEEE International Conference on 1997: 610 –611. [20] R. Basri, L. Costa, D. Geiger, and D. Jacobs, “Determining the Similarity of Deformable Objects,” Vision Research, 38(15-16): 2,365-2,385, 1998. [21] M. Donahue, D. Geiger, R. Hummel, and T. Liu, “Sparse Representations for Image Decompositions with Occlusions,” Proc. IEEE Conf. Computer Vision and Pattern Recognition: 7-12, 1996. [22] Y. Gdalyahu and D. Weinshall, “Flexible Syntactic Matching of Curves and Its Application to Automatic Hierarchical Classification of Silhouettes,” IEEE Trans. Pattern Analysis and Machine Intelligence, 21(12): 1,312-1,328, Dec. 1999. [23] D. Huttenlocher, G. Klanderman, and W. Rucklidge, “Comparing Images Using the Hausdorff Distance,” IEEE Trans. Pattern Analysis and Machine Intelligence, 15(9): 850-863, Sept. 1993. [24] J. Puzicha, J. Buhmann, Y. Rubner, and C. Tomasi, “Empirical Evaluation of Dissimilarity Measures for Color and Texture,” Proc. Int'l Conf. Computer Vision: 1,165-1,172, 1999. [25] M. Stricker and M. Swain, “The Capacity of Color Histogram Indexing,” Proceedings of IEEE CVPR’94: 704-708. [26] R. Brunelli and O. Mich, “Histograms analysis for image retrieval,” Pattern Recognition, 34: 1625-1637, 2001. [27] M. Carbon and L. T. Tran, “On histograms for linear processes,” Journal of Statistical Planning and Inference, 53: 403-419, 1996. [28] E. Oja, K. Valkealahti, “Co-occurrence map: Quantizing multidimensional texture Histograms,” Pattern Recognition Letter, 17: 723-730, 1996. [29] Y. Linde, A. Buzo and R. Gray, “An algorithm for vector quantizer design,” IEEE Trans. Communication, 28(1): 84-95, 1980. [30] L. J. Liu and Y. H. Yang, “Multi-resolution color image segmentation,” IEEE Trans. PAMI, 16: 689-700, 1994. [31] C. E. Jacobs, Adam Finkelstein, and D. H. Salesin, “Fast Multi-resolution Image Querying”, Department of Computer Science and Engineering, University of Washington. [32] S. Louvel and J. F. Chamayou, “Packing and depacking histograms with statistical processing,” Computer Physics Communications 93:289-302, 1996. [33] X. Wan and C. -C. Jay Kuo, “A New Approach to Image Retrieval with Hierarchical Color Clustering,” IEEE Trans. on Circuits and Systems for Video Technology, 8(5), 1998. [34] C. L. Novak and S. A. Shafer, “Anatomy of a Color Histogram,” Proceedings of the 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '92): 599 - 605, June 1992. [35] M. Greenwald, “Practical Algorithms for Self Scaling Histograms or Better than Average Data Collection,” Performance Evaluation, 27/28: 19-40, 1996. [36] F. Ennesser and G. Medioni, “Finding Waldo, or focus of attention using local color information,” IEEE Trans. Pattern Analysis and Machine Intelligence, 17(8): 805-809, 1995. [37] G. Pass, R. Zabih, and J. Miller, “Comparing images using color coherence vectors,” Proceedings of ACM Multimedia'96: 65-73, 1996. [38] G. Pass and R. Zabih, “Histogram refinement for content-based image retrieval,” Applications of Computer Vision: 96 –102, 1996. [39] T. S. Chua and M. Kankanhalli, “Towards Pseudo-object Models for Content-based Visual Information Retrieval,” Intern. Symposium on Multimedia Information Proceeding’98: 182-192, 1998. [40] T. S. Chua and C. X. Chu, “Color-based pseudo object model for Image retrieval with relevance feedback,” 1st International Conference on Advanced Multimedia Content Processing: 148-162, Nov. 1998. [41] K.Valkealahti and E. Oja, “Reduced multidimensional co-occurrence histograms in texture classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, 20(1): 90-94, 1998. [42] J. R. Smith and S. F. Chang, “Tools and techniques for color image retrieval,” IS&T/SPIE proceedings, 2670, storage & retrieval for image video database IV, 1996. [43] J. R. Smith and S. F. Chang, “Automated Image Retrieval Using Color and Texture,” Columbia Technical Report, 1995. [44] V. V. Vinod and H. Murase, “Focused Color Intersection with Efficient Searching for Object Extraction,” Pattern Recognition, 30(10): 1787-1797, 1997. [45] B. M. Mehtre, M. S. Kankanhalli, A. D. Narasimhalu, and G. C. Man, “Color Matching for Image Retrieval,” Pattern Recognition Letter, 16: 325-331, 1995. [46] G. Yihong, Z. Hongjiang, C. H. Chuan, and M. Sakauchi, “An Image Database System with Contents Capturing and Fast Image Indexing Abilities,” Proceedings of the International Conference on Multimedia Computing and Systems: 121-130, Boston, Massachusetts, May 14-19, 1994. [47] C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D. Petkovic, and W. Equitz, “Efficient and effective querying by Image content,” Journal of Intelligent Information Systems, 3(3): 231-262, 1994. [48] In. K. Park, I. D. Yun, and S. U. Lee, “Color image retrieval using hybrid graph representation,” Image and Vision Computing, 17: 465–474, 1999. [49] S. R. Fountain and T. N. Tan, “Efficient rotation invariant texture features for content-based image retrieval,” Pattern Recognition, 31(11): 1725-1732, 1998. [50] E. H. Liang and D. L. Mou, “An algorithm of computing spatial similarity between images,” Proceeding of IPPR Conference on Computer Vision, Graphics and Image Processing: 115-121, Taiwan, 1997. [51] N. Shiotam and S. Miyamoto, “Image Retrieval System Using an Iconic Thesaurus,” IEEE International Conference on Intelligent Processing Systems, Oct. 28-31, Beijing, China. [52] C. Carson, S. Belongie, H. Greenspan, and J. Maliky, “Region-Based Image Querying,” Computer Science Division University of California, Berkeley. [53] J. R. Smith and C. S. Li, “Image Classification and Querying Using Composite Region Templates,” Computer Vision and Image Understanding, 75(1/2): 165–174, July/August, 1999. [54] P. Lipson, E. Grimson and P. Sinha, “Configuration Based Scene Classification and Image Indexing,” MIT Artificial Intelligence Lab. [55] W. Grosky and R. Methrotra, Guest Editors, “Special Issue on Image Database Management,” IEEE Computer, 22(12), 1989. [56] Ralescu and R. Jain, Guest Editors, Special Issue on Advances in Visual Information Management Systems, J. Intelligent Information Systems, 3(3), 1994. [57] V. N. Gudivada and V. V. Raghavan, Guest Editors, “Special Issue on Content-Based Image Retrieval Systems,” IEEE Computer, 28(9), 1995. [58] A. D. Narasimhalu, “Special Issue on Content-Based Retrieval,” ACM Multimedia Systems, 3(1), 1995. [59] Y. Rui and T. S. Huang, “Image retrieval: current techniques, promising directions, and open issues”, Journal of Visual Comm. and Image representation, 10: 39-62, 1999. [60] Brink A., Marcus Sherry, and Subrahmanian V. S., “Heterogeneous Multimedia Reasoning,” IEEE Computer, 28(9): 33-39, 1995. [61] Jajodia S. and Subrahmanian V. S. Eds, “Multimedia Database Systems: Issues and Research Directions,” Springer-Verlag, New-York, 1995. [62] Srihari R. K. and Burhans D. T., “Visual Semantics: Extracting Visual Information from Text Accompanying Pictures,” Proc. AAAI 94: 793-798, 1995. [63] Y. Rui, T. S. 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, 8(5): 644-655, 1998. [64] W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos, and G. Taubin, "The QBIC project: querying Images by content using color, texture, and shape," Proc. Storage and Retrieval for Image and Video Databases, vol. 1908: 173-187, 1993. [65] M. Flickner, H. Sawhney, W. Niblack, 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 system: The QBIC system,” IEEE Computer, 28(9): 23-32, 1995. [66] H. J. Zhang, J. Wu, D. Zhong, S. W. Smoliar, “An Integrated system for content-Based video retrieval and browsing,” Pattern recognition, 30(4): 643-658, 1997. [67] Herzog, A. Miene, TH. Hermes, P. Alshuth “Integrated information mining for tex, images, and video,” Computer & Graphics, 22(6): 675-685, 1998. [68] Srihari R. K., "Use of collateral text in understanding photos," Artificial Intelligence Review, 8(5): 409-430, 1995. [69] Srihari R. K., “Automatic Indexing and Content-Based Retrieval of Captioned Images,” IEEE Computer, 28(9): 49-56, 1995. [70] J. R. Smith and S. F. Chang, “Quad-Tree Segmentation for Texture-based Image Query,” ACM Multimedia'94, 1994. [71] H. C. Lin, L. L. Wang and S. N. Yang, “Color Image Retrieval Based on Hidden Markov Models,” IEEE Trans. Image Processing, 6(2): 332-339, 1997. [72] A. K. Jain, and A. Vailaya, “Image Retrieval Using Color and Shape,” Pattern Recognition, 29(8): 1233-1244, 1996. [73] W. S. Lin and S. Y. Chen, Robust image retrieval using color and shape, The master thesis of Electrical Engineering and Computer Engineering and Science of Yuan-Ze Institute of Technology, Taiwan, R.O.C., 1997. [74] C. Schmid and R. Mohr, “Local Grayvalue Invariant for Image Retrieval,” IEEE PAMI., 19(5), May. 1997. [75] W. I. Grosky and R. Methrotra, "Index-based object recognition in pictorial data management," Computer Vision, Graphics, and Image Processing, 52(3): 416-436, 1990. [76] R. Methrotra and J. E. Gary, "Similar-shape retrieval in shape data management," IEEE Computer, 28(9): 57-62, 1995. [77] L. K. Huang and M. J. J. Wang, “Efficient shape matching through model-based shape recognition, ” Pattern Recognition, 29(2): 207-215, 1996 [78] A. M. N. Fu and H. Yan, “Effective classification of planar shapes based on curve segment properties,” Pattern Recognition Letter, 18: 55-61, 1997. [79] Q. M. Tieng and W. W. Boles, “Recognition of 2d object contours using the wavelet transform zero-crossing representation”, IEEE Trans. on PAMI, 19(8): 910-916, 1997. [80] G. Lu, “An approach to image retrieval based on shape,” Journal of Information Science, 23(2): 119-127, 1997. [81] G. Cortelazzo, G. A. Main, G. Vezzi, and P. Zamperoni, “Trademark Shapes Description by String Matching Techniques,” Pattern Recognition, 27(8): 1005-1018, 1994. [82] A. K. Jain and A. Vailaya, “Shape-based retrieval: a case study with trademark image databases,” Pattern Recognition, 31(9): 1369-1390, 1998. [83] H. H. Chen, Video indexing Using Color Histogram and Camera Operation, MS thesis, Department of Computer Science and Information Engineering, National Central University, 2000. [84] Y. Rui, T. S. Huang, and S. Mehrotra, “Exploring video structures beyond the shots,” Proc. of IEEE conf. Multimedia Computing and Systems, Austin, Texas USA, 1998. [85] G. Lupatini, C. Saraceno, R. Leonardi, “Scene break detection: a comparison,” proceeding 1998 Continuous-Media Database and Application: 34-41, 1998. [86] H. Jiang, A. Helal, A. K. Elmagarmid, A. Joshi, “Scene change detection techniques for video database systems,” Multimedia System, 1998. [87] J. M. Corridoni, A Del Bimbo, “Structured representation and automatic indexing of movie information content,” Pattern Recognition, 31(12): 2027-2045, 1998. [88] A. Akutsu, Y. Tonomura, H. Hashimoto, and Y. Ohba, “Video indexing using motion vectors,” SPIE Vol.1818 Visual Communications and Image Processing: 1522-1530, 1992. [89] H. J. Zhang, A. Kankanhalli, and S. W. Smoliar, “Automatic partitioning of full-motion video,” Multimedia System, 1: 10-28, 1993. [90] E. Ardizzone, M. L. Cascia, D. Molinelli, “Motion and Color-Based video Indexing and Retrieval, ” 1996 IEEE proceeding of ICPR: 135-139. [91] K. J. Han and A. H. Tewfik, “Eigen-Image Based Video Segmentation and index, ” Image Processing, Proceeding of international conference on published 1997, 4: 538-541, 1997. [92] J. D. Courtney, “Automatic video indexing via object motion analysis,” Pattern Recognition, 30(4): 607-625, 1997. [93] J. M. Gauch, S. Gauch, S. Bouix, and X. L. Zhu, “Real time video scene detection and classification,” Information Processing and Management, 35: 401-420, 1995. [94] A. Jain, A. Vailaya, W. Xiong, “Query by video clip,” Pattern recognition, 1998 proceedings, 14th International conference on Published, 1: 909-911. [95] M. Irani, P. Anandan, J. Bergen, R. Kumar, and S. Hsu, “Efficient representations of video sequences and their applications,” Signal Processing: Image Communication, 8(4): 327-351, May 1996. [96] N. V. Patel and I. K. Sethi, “Video shot detection and characterization for video databases,” Pattern Recognition, 30(4): 583-592, 1997. [97] R. Zabih, J. Miller, and K. Mai, “Video browsing using edges and motion,” Computer Vision and Pattern Recognition, Proceedings CVPR '96, 1996 IEEE Computer Society Conference: 439 – 446, 1996. [98] M. S. Lee, B. W. Hwang, S. Sull, and S. W. Lee, “Automatic video parsing using shot boundary detection and camera operation analysis,” Pattern Recognition, Fourteenth International Conference Proceedings, 2: 1481–1483, 1998. [99] Y. Zhuang, Y. Rui, T. S. Huang, and S. Mehrotra, “Adaptive key frame extraction using unsupervised clustering,” Proceedings of IEEE International Conference on Image Processing, Chicago, IL, Oct. 4-7 1998. [100] P. O. Gresle and T. S. Huang, “Gusting of video documents: A key frames selection algorithm using relative activity measurement,” The 2nd International Conference on Visual Information Systems, 1997. [101] D. Zhong, H. J. Zhang and S. F. Chang, “Clustering Methods for Video Browsing and Annotation,” SPIE Proceedings, vol. 2670: 239-246, 1996. [102] W. Wolf, “Key frame selection by motion analysis,” ICASSP-96, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2: 1228 -1231, 1996. [103] G. Iyengar and A. B. Lippman, “Semantically controlled content-based retrieval of video,” Multimedia Storage and Archiving III, Voice, Video and Data 98, Boston, Nov.r 1998. [104] H. Palus and D. Bereska, “The Comparison between transformations from RGB color space to HIS color space, used for object recognition,” image processing and its applications 4-6 July 1995 conference publication. [105] J. L. Barron, D. J. Fleet, and S. S. Beauchemin, “Performance of optical flow techniques,” International journal of computer vision, 12(1): 23-77, 1994. [106] E. Ardizzone and M. La Cascia, “Video indexing using optical flow field,” International Conference on Image Processing, 3: 831-34, 1996. [107] A. S. Abutaleb, “Automation thresholding of gray level pictures using two-dimensional entropy,” Computer Vision Graphic Image Process, 47: 22-32, 1989. [108] A. D. Brink, “Thresholding of digital Images using two-dimensional entropies,” Pattern Recognition, 25: 803-808, 1992. [109] H. S. Sawhney and S. Ayer, “Compact representations of videos through dominant and multiple motion estimation,” IEEE Trans. PAMI, Aug. 1996. [110] E. H. Adelson and J. Y. A. Wang, “Representing moving images with layers,” Technical Report Media Lab. Vision and Modeling Group, TR no. 279, MIT, Nov. 1993. [111] J. Friedman, J. Bently, and R. Finkel, “An Algorithm for Finding Best Matches in Logarithmic Expected Time,” ACM Trans. Math. Software, 3(3): 209-226, 1977. [112] M. S. Kankanhalli, B. M. Mehtre, and J. K. Wu, “Cluster-Based Color Matching for Image Retrieval,” Pattern Recognition 29(4): 701-708, 1996.
指導教授	范國清(Kuo-Chin Fan)	審核日期	2003-7-18
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