臺灣博碩士論文加值系統

本論文提出「基於影像區域視覺群組之修正式一般化霍福轉換及其視覺物件檢索之應用」。在許多論文及著作中，語意導向的影像內容檢索都是針對區域式影像檢索來做討論。但這些方法有一個常見的缺點，就是同構的切割區域與影像的語意概念是不容易建立關聯的。以至於會造成這些依據低階視覺特徵的區域式檢索方法效果都不是很好。而透過查詢多媒體資料庫以找尋存放於資料庫影像中的視覺物件，一直是大家亟欲想解決的問題，但時至今日仍沒有一個公認的有效方法出現，所以這個議題對大家來說仍舊是一個挑戰。且在這個問題中，若欲查詢影像物件做了位移、旋轉和縮放，則又會增加檢索的困難度。電腦視覺應用在高階語意分析的影像檢索中，物件的切割與辨識是很重要的一步。然而，在沒有辦法預知物件形狀和得到物件相關資料的情況下，物件的自動切割與自動辨識是一個很困難的工作。因此，我們的研究主要是應用電腦視覺的方法，透過影像區域的視覺群組來討論資料庫中影像物件的結構，並以此來代替對影像切割區域的低階特徵描述。在文中我們設計了一個投票系統，修改一般化霍福轉換以應用在物件的搜尋上，這個投票系統使得影像物件的位移、旋轉、縮放參數可以在短時間內成為不變量，因此可快速的找出資料庫中和欲查詢物件相似的影像。最後我們以電腦模擬證明我們所提出的方法在檢索的準確性和效能都有很好的表現。

This paper presents an object-based image retrieval using a method based on perceptual grouping of image regions using the modified generalized Hough transform. The effectiveness of region-based representation for content-based image retrieval is extensively studied in the literature. One common weakness of the region-based approaches is that the homogeneous image regions have little correspondence to the semantic image concepts, thus, the retrieval results of region-based approaches in terms of regions’ low-level visual features are far from satisfactory. It is desirable and yet remains as a challenge for querying multimedia data by finding an object inside a target image. Given an object model, an added difficulty is that the object might be translated, rotated, and scaled inside a target image. Object segmentation and recognition is the primary step of computer vision for applying to image retrieval of higher-level image analysis. However, automatic segmentation and recognition of objects via object models is a difficult task without a priori knowledge about the shape of objects. Instead of segmentation and detailed object representation, the objective of this research is to develop and apply computer vision methods that explore the structure of an image object by perceptual grouping of image regions to retrieve images from a database. A voting scheme based on generalized Hough transform is proposed to provide object search method, which is invariant to the translation, rotation, scaling of image data, and hence, invariant to orientation and position. Computer simulation results show that the proposed method gives good performance in terms of retrieval accuracy and robustness.

中文摘要 --------------------------------------- i
英文摘要 --------------------------------------- ii
誌謝 ------------------------------------------- iii
目錄 ------------------------------------------- iv

壹、緒論-------------------------------------------- 1
1.1 研究背景------------------------------------- 1
1.2 研究目的------------------------------------- 5
1.3 論文架構------------------------------------- 7

貳、基於語意的影像檢索(CBIR)之文獻探討-------------- 8
2.1 影像切割------------------------------------- 9
2.2 低階特徵與影像檢索系統---------------------- 11
2.2.1 顏色特徵---------------------------------- 12
2.2.2 形狀特徵---------------------------------- 13
2.2.3 紋理特徵---------------------------------- 16
2.2.4 空間關係特徵------------------------------ 20
2.3 語意差異的彌合與高階影像檢索系統------------ 20
2.3.1 物件 Ontology----------------------------- 21
2.3.2 機器學習---------------------------------- 23
2.3.3 使用者回饋 (Relevance Feedback (RF))------ 27
2.3.4 建立語意樣板 (Semantic Template (ST))----- 28
2.3.5 Web影像檢索------------------------------- 29
2.4 國內外相關研究------------------------------ 31

參、基於區域的修正式一般化霍福轉換----------------- 37
3.1 直線偵測的霍福轉換-------------------------- 38
3.2 圓形偵測的霍福轉換-------------------------- 42
3.3 一般化的(Generalized)霍福轉換--------------- 43
3.3.1 樣板建立階段------------------------------ 44
3.3.2 形狀偵測階段(voting)---------------------- 45
3.3.3 加入旋轉及縮放的考慮---------------------- 45
3.4 區域式的一般化霍福轉換---------------------- 46

肆、本論文提出的視覺物件搜尋演算法----------------- 48
4.1 物件選擇與比對的方法------------------------ 48
4.2 以矩量保存法進行顏色區域的比對-------------- 53
4.3 為計算幾何轉換參數所設計的投票機制---------- 56
4.4 物件幾何轉換參數的驗證---------------------- 58

伍、系統實現與實驗結果----------------------------- 60
5.1 程式介面------------------------------------ 60
5.2 實驗設計------------------------------------ 62
5.2.1 實驗一------------------------------------ 62
5.2.2 實驗二------------------------------------ 64
5.2.3 實驗三------------------------------------ 65
5.2.4 實驗四------------------------------------ 67
5.3 實驗分析與改進方向-------------------------- 71

陸、結論與未來展望--------------------------------- 74
6.1 未來展望------------------------------------ 75
6.2 對於學術研究及其他應用預期之貢獻------------ 76

柒、參考文獻--------------------------------------- 77

1. Ying Liu, Dengsheng Zhang, Guojun Lu, Wei-Ying Ma, A survey of content-based image retrieval with high-level semantics, Pattern Recognition, Vol. 40, Issue 1, January 2007, pp. 262-282.

2. F. Jing, M. Li, L. Zhang, H.-J. Zhang, B. Zhang, Learning in region-based image retrieval, Proceedings of the International Conference on Image and Video Retrieval (CIVR2003), 2003, pp. 206–215.

3. H. Feng, D.A. Castanon, W.C. Karl, A curve evolution approach for image segmentation using adaptive flows, Proceedings of the International Conference on Computer Vision (ICCV’01), 2001, pp. 494–499.

4. W.Y. Ma, B.S. Majunath, Edge flow: a framework of boundary detection and image segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997, pp. 744–749.

5. J. Shi, J. Malik, Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 22 (8) 2000, pp. 888–905.

6. D. Comaniciu, P. Meer, Robust analysis of feature spaces: color image segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1997, pp. 750–755.

7. P.L. Stanchev, D. Green Jr., B. Dimitrov, High level color similarity retrieval, Int. J. Inf. Theories Appl. 10 (3) 2003, pp. 363–369.

8. K.A. Hua, K. Vu, J.-H. Oh, SamMatch: a flexible and efficient sampling-based image retrieval technique for large image databases, Proceedings of the Seventh ACM International Multimedia Conference (ACM Multimedia '99), November 1999, pp. 225–234.

9. Y. Deng, B.S. Manjunath, Unsupervised segmentation of color-texture regions in images and video, IEEE Trans. Pattern Anal. Mach. Learn. (PAMI) 23 (8) 2001, pp. 800–810.

10. H. Feng, T.-S. Chua, A bootstrapping approach to annotating large image collection, Workshop on Multimedia Information Retrieval in ACM Multimedia, November 2003, pp. 55–62.

11. Y. Liu, D.S. Zhang, G. Lu, W.-Y. Ma, Region-based image retrieval with perceptual colors, Proceedings of the Pacific-Rim Multimedia Conference (PCM), December 2004, pp. 931–938.

12. C. Carson, S. Belongie, H. Greenspan, J. Malik, Blobworld: image segmentation using expectation-maximization and its application to image querying, IEEE Trans. Pattern Anal. Mach. Intell. 8 (8) 2002, pp. 1026–1038.

13. R. Shi, H. Feng, T.-S. Chua, C.-H. Lee, An adaptive image content representation and segmentation approach to automatic image annotation, International Conference on Image and Video Retrieval (CIVR), 2004, pp. 545–554.

14. S. C. Cheng, Region-growing approach to color segmentation using 3-D clustering and relaxation labeling, IEE Proc.-Vis. Image Signal Process., Vol. 150, N0. 4, August 2003, pp. 270-276.

15. M. Stricker and M. Orengo, Similarity of color images, SPIE Storage and Retrieval for Image and Video Databases III, vol. 2185, Feb. 1995, pp.381-392.

16. C.P. Town, D. Sinclair, Content-based image retrieval using semantic visual categories, Society for Manufacturing Engineers, Technical Report MV01-211, 2001.

17. V. Mezaris, I. Kompatsiaris, M.G. Strintzis, An ontology approach to object-based image retrieval, Proceedings of the ICIP, vol. II, 2003, pp. 511–514.

18. K.N. Plataniotis, A.N. Venetsanopoulos, Color Image Processing and Applications, Springer, Berlin, 2000.

19. Minh, N. Do, and Martin, Vetterli, Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance, IEEE Transactions on image processing, Vol. 11, No. 2, Feb., 2002.

20. Reed, T.R. and J.M., A review of recent texture segmentation and feature extraction techniques, CVGIP: Image Understanding, Vol. 57, May, 1993, pp. 359-372.

21. A. K. Jain, Fundamentals of Digital Image Processing, Prentice-Hal, Englewood CliJs, NJ, 1989.

22. H. Tamura, S. Mori, and T. Yamawaki, Texture features corresponding to visual perception, IEEE Trans. On Systems, Man, and Cybernetics, vol. Smc-8, no. 6, June 1978.

23. W. Y. Ma and B. S. Manjunath, A comparison of wavelet features for texture annotation, Proc. of IEEE Int. Conf. on Image Processing, vol. II, , Washington D.C. Oct. 1995, pp. 256-259.

24. J. G. Daugman, Complete discrete 2D Gabor transforms by neural networks for image analysis and compression, IEEE Trans. ASSP, vol. 36, July 1998, pp. 1169-1179.

25. A. C. Bovic, M. Clark, and W. S. Geisler, Multichannel texture analysis using localized spatial filters, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, January 1990, pp. 55-73.

26. A. K. Jain and F. Farroknia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, 24(12), 1991, pp. 1167-1186.

27. K. S. Thyagarajan, Tom Nguyen, and Charles Persons, A maximum likelihood approach to texture classification using wavelet transform. In Proc. IEEE Int. Conf. on Image Proc., 1994.

28. M. K. Hu, Visual pattern recognition by moment invariants, in J. K. Aggarwal, R. O. Duda, and A. Rosenfeld, Computer Methods in Image analysis, IEEE computer Society, Los Angeles, CA, 1977.

29. D. Cai, X. He, Z. Li, W.-Y. Ma, J.-R. Wen, Hierarchical clustering of WWW image search results using visual, textual and link information, Proceedings of the ACM International Conference on Multimedia, 2004.

30. D. Cai, X. He, W.-Y. Ma, J.-R. Wen, H. Zhang, Organizing WWW images based on the analysis of page layout and web link structure, Proceedings of the International Conference on Multimedia and Expo (ICME), Taipei, 2004.

31. J. Ren, Y. Shen, L. Guo, A novel image retrieval based on representative colors, Proceedings of the Image and Vision Computing, N.Z., November 2003, pp. 102–107.

32. S. Kulkarni, B. Verma, Fuzzy logic for texture queries in CBIR, Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), Xi’an, China, 2003, pp. 223–226.

33. C.-Y. Chiu, H.-C. Lin, S.-N. Yang, Texture retrieval with linguistic descriptors, IEEE Pacific Rim Conference on Multimedia, 2001, pp. 308–315.

34. Y. Zhuang, X. Liu, Y. Pan, Apply semantic template to support content-based image retrieval, Proceedings of the SPIE, Storage and Retrieval for Media Databases, vol. 3972, December 1999, pp. 442–449.

35. M. Obeid, B. Jedynak, M. Daoudi, Image indexing and retrieval using intermediate features, Proceedings of the Ninth ACM International Conference on Multimedia, Ottawa, Canada, 2001, pp. 531–533.

36. D.M. Conway, An experimental comparison of three natural language color naming models, Proceedings of the East–West International Conference on Human-Computer Interactions, St. Petersburg, Russia, 1992, pp. 328–339.

37. T. Berk, L. Brownston, A. Kaufman, A new color-naming system for graphics language, IEEE Computer Graphics Appl. 2 (3) 1982, pp. 37–44.

38. A.R. Rao, G.L. Lohse, Towards a texture naming system: identifying relevant dimensions of texture, IEEE Proceedings of the Fourth Conference on Visualization, 1993, pp. 220–227.

39. A. Vailaya, M.A.T. Figueiredo, A.K. Jain, H.J. Zhang, Image classification for content-based indexing, IEEE Trans. Image Process. 10 (1) 2001, pp. 117–130.

40. J. Luo, A. Savakis, Indoor vs outdoor classification of consumer photographs using low-level and semantic features, International Conference on Image Processing (ICIP), vol II, October 2001, pp. 745–748.

41. V.N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.

42. W. Jin, R. Shi, T.-S. Chua, A semi-naı‥ve bayesian method incorporating clustering with pair-wise constraints for auto image annotation, Proceedings of the ACM Multimedia, 2004.

43. L. Zhang, F. Liu, B. Zhang, Support vector machine learning for image retrieval, International Conference on Image Processing, October 2001, pp. 7–10.

44. S. Tong, E. Chang, Support vector machine active learning for image retrieval, Proceedings of the ACM International Conference on Multimedia, Ottawa, Canada, 2001, pp. 107–118.

45. D. Stan, I.K. Sethi, Mapping low-level image features to semantic concepts, Proceedings of the SPIE: Storage and Retrieval for Media Databases, 2001, pp. 172–179.

46. M. Bilenko, S. Basu, R.J. Mooney, Integrating constraints and metric learning in semi-supervised clustering, Proceedings of the 21st International Conference on Machine Learning (ICML), July 2004, pp. 81–88.

47. Y. Chen, J.Z.Wang, R.Krovetz, An unsupervised learning approach to content-based image retrieval, IEEE Proceedings of the International Symposium on Signal Processing and its Applications, July 2003, pp. 197–200.

48. X. Zheng, D. Cai, X. He, W.-Y. Ma, X. Lin, Locality preserving clustering for image database, Proceedings of the 12th ACM Multimedia, October 2004.

49. G. Salton, Automatic Text Processing, Addison-Wesley, Reading, MA, 1989.

50. Y. Rui, T.S. Huang, M. Ortega, S. Mehrotra, Relevance feedback: a power tool for interactive content-based image retrieval, IEEE Trans. Circuits Video Technol. 8 (5) 1998, pp. 644–655.

51. Y. Rui, T.S. Huang, Optimizing learning in image retrieval, Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, June 2000, pp. 1236–1243.

52. X.S. Zhu, T.S. Huang, Relevance feedback in image retrieval: a comprehensive review, Multimedia System 8 (6) 2003, pp. 536–544.

53. J.R. Smith, C.-S. Li, Decoding image semantics using composite region templates, IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL-98), June 1998, pp. 9–13.

54. S.-F. Chang, W. Chen, Semantic visual templates: linking visual features to semantics, International Conference on Image Processing (ICIP), Workshop on Content Based Video Search and Retrieval, vol. 3, October 1998, pp. 531–534.

55. G. Miller, R. Beckwith, C. Fellbaum, D. Gross, K. Miller, Introduction to Wordnet: an on-line lexical database, Int. J. Lexicography 3 1990, pp. 235–244.

56. Barnard K, Duygulu P, Forsyth D, Clustering Art. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2001, pp. 434-439

57. Srihari R K, Rao A B, Han B, Munirathnam S, Wu X Y. A Model for Multimodal Information Retrieval. IEEE International Conference on Multimedia & Expo. 2000.

58. Zhou X S, Huang T S, Unifying Keywords and Visual Contents in Image Retrieval, IEEE Multi Media, 2002.

59. C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D. Petkovic, W. Equitz, Efficient and effective querying by image content, J. Intell. Inf. Syst. 3 (3–4) 1994, pp. 231–262.

60. A. Pentland, R.W. Picard, S. Scaroff, Photobook: content-based manipulation for image databases, Int. J. Computer Vision 18 (3) 1996, pp. 233–254.

61. J.R. Smith, S.F. Chang, VisualSeek: a fully automatic content-based query system, Proceedings of the Fourth ACM International Conference on Multimedia, 1996, pp. 87–98.

62. J.Z. Wang, J. Li, G. Wiederhold, SIMPLIcity: semantics-sensitive integrated matching for picture libraries, IEEE Trans. Pattern Anal. Mach. Intell. 23 (9) 2001, pp. 947–963.

63. Andrea F. Abate, Michele Nappi, Genny Tortora, and Maurizio Tucci. IME: an image management environment with content-based access, Image and Vision Computing 17, 1999, pp.967-980.

64. Nevenka, Thomas McGee, and Herman Elnbaas, Video Keyframe Extraction and Filtering : A Keyframe is not a Keyframe to Everyone, Proceedings of the 103 sixth international conference on Information and Knowledge management, 1997, pp. 113-120.

65. http://fishdb.sinica.edu.tw/

66. http://turing.csie.ntu.edu.tw/ncnudlm/index.html

67. D. Ballard, Generalizing the Hough transform to detect arbitrary shapes, Pattern Recognition, 13 (2) 1981, pp. 111-122.

68. C.P Chau and W.C. Siu, Generalized Hough Transform Using Regions with Homogeneous Color, Int. J. Computer Vision 59 (2) 2004, pp. 183–199.