Responsive image
博碩士論文 etd-0702106-211024 詳細資訊
Title page for etd-0702106-211024
論文名稱
Title
影像註解之資訊探勘方法
Information Mining of Image Annotation
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
96
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2006-06-26
繳交日期
Date of Submission
2006-07-02
關鍵字
Keywords
資訊探勘、影像註解、碎形
fractal, image annotation, image information mining
統計
Statistics
本論文已被瀏覽 5661 次,被下載 1431
The thesis/dissertation has been browsed 5661 times, has been downloaded 1431 times.
中文摘要
傳統的content-based image retrieval system,有些是利用顏色、形狀、紋理來做搜尋影像內容的依據,但對於一般的使用者來說,利用這些低階的特徵來做搜尋是有困難的,而且大部分的使用者比較偏好利用文字來做搜尋。例如,Google的image search 雖然它的名字叫做image search ,但事實上卻是一種標記的搜尋,是靠影像的註記來做搜尋,而不是以影像的內容做為搜尋的依據。隨著對影像註記的需求愈來愈殷切,MEPG 7訂定Multimedia Description Schemes (DSs)影音註解的標準,可是目前影像的註解,大部分還是要依靠人力,非常的耗時,如何對於影像下適當而且自動的註解是非常重要的。所以我們提出一個方法可以對影像做自動的註記,我們的方法是擷取出每一張影像的fractal features,再利用Diverse Denisty Algorithm做為訓練分類的方法,讓使用者和系統可以做即時性的互動式學習,最後可以利用已經訓練好的models對影像做自動註解。
Abstract
Traditional Content-based image retrieval supports image searches based on color, texture and shape. However it is difficult and nonintuitive for most user to use those low level features to query images. And for most user they like search by keywords . For example , recently Google provide services in image search. Although it is named image search , but actually it is search by keywords ,not image-contents. For this reason MPEG-7 now support textual annotation standard which is MPEG-7 Multimedia Description Schemes (DSs) are metadata structures for describing and annotating audio-visual (AV) content. But manual annotation of image or video take time and expensive. we propose a system which could help us to make suitable auto-annotations.We extract the image factal features and use Diverse Density Algorithm for training models. In this way , user and system can interact in real-time . When trained models in database is growing, the system auto-annotation success rate is increasing.
目次 Table of Contents
摘要 i
Abstract ii
目錄 iii
圖目錄 vi
表目錄 x
第1章 簡介 1
1.1 Image Information Mining 1
1.2 Annotation相關研究 8
1.2.1 人工註解(annotation) 9
1.2.2 訓練類別(training) 11
1.2.3 自動註解(automatic annotation) 12
1.2.4 特徵擷取 12
1.3 研究概述 19
第2章 碎形理論 21
2.1 轉換之收歛性 25
2.2 迭代函數系統 (Iterative Function System) 25
2.3 影像分割 27
2.4 分割版的迭代函數(Partitioned Iterated Function System) 28
2.4.1 影像碎形壓縮法—編碼端: 32
2.4.2 影像碎形壓縮法—解碼端: 33
2.5 Orthogonal Basis IFS 34
第3章 Diverse Density Algorithm 40
3.1 定義 40
3.2 Diverse Density Algorithm 41
3.2.1 Diverse Density definition: 43
3.2.2 計算 44
3.2.3 計算 46
3.2.4 Finding the maximum 46
3.2.5 例子 49
第4章 研究方法及結果 51
4.1 研究方法 51
4.2 步驟 52
4.2.1 資料庫建立 53
4.2.2 註解的文字 57
4.2.3 碎形編碼(Orthonormal IFS) 58
4.2.4 比對方法 60
4.2.5 使用Diverse Density找出感興趣之特徵 64
4.2.6 Proper Model 68
4.2.7 Not Proper Model 69
4.3 實驗結果 72
第5章 結論 75
附錄 76
參考文獻 80
參考文獻 References
[1] R. Zaïane, J. Han, Z.-N. Li, and J. Hou. “Mining multimedia data,” in Proc. CASCON'98, pp. 83-96, Nov. 1998.
[2] O. El Badawy, MR El-Sakka, K. Hassanein, and M. Kamel, “Image data mining from financial documents based on wavelet features,” in Proc. IEEE ICIP-2001, vol. 1, pp. 1078-1081, Oct. 2001.
[3] Jiang Li and Ram M. Narayanan, “Integrated spectral and spatial information mining in remote sensing imagery,” IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, Mar. 2004.
[4] E. Chang, G. Kingshy, G. Sychay, and G. Wu, “CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 1, Jan. 2003.
[5] P. Duygulu, K. Barnard, N. de Freitas, and D. Forsyth , “Object recognition as machine translation: learning a lexicon for a fixed image vocabulary,” in Proc. ECCV'02, pp. IV:97-112, 2002.
[6] Lilian HY Tang, Rudolf Hanka, and Horace Ho-Shing Ip, “Histological image retrieval based on semantic content analysis,” IEEE Trans. Inf. Technol. Biomed., vol. 7, no. 1, Mar. 2003.
[7] J. Li and J. Z. Wang, “Automatic linguistic indexing of pictures by a statistical modeling approach,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 9, Sept. 2003.
[8] D. Androutsos, K.N. Plataniotis, and A.N. Ventsanopoulos, “A novel vector-based approach to color image retrieval using a vector angular-based distanced measure,” CVIU'99, vol. 75, nos. 1/2, pp. 46-58, 1999.
[9] G.D. Finlayson, S.S. Chatterjee, and B.V. Funt, “Color angular indexing,” ECCV'96, vol. 11, pp. 16-27, 1996.
[10] S.C. Pei and C.M. Cheng, “Extracting color features and dynamic matching for image data-base retrieval,” IEEE Trans. Circuits Syst. Video Technol., vol. 9, no. 3, pp. 501-512, 1999.
[11] M.J. Swain and D.H. Ballard, “Color index,” IJCV'91, vol. 7, no. 1, pp. 11-32, 1991.
[12] B. M. Mehtre, M. S. Kankanhalli, A. D. Narasimhalu, and G. C. Man, “Color matching for image retrieval,” Patte. Recognit. Lett., vol. 16, pp. 325-331, 1995.
[13] X. Wan and C.C.J Kuo, “A new approach to image retrieval with hierarchical color clustering,” IEEE Trans. Circuits Syst. Video Technol., vol. 8, no. 5, pp. 628-643, 1998.
[14] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing. New York: Addision-Wesley, 1992.
[15] John C. Russ, The Image Processing Handbook. New York: CRC Press, 1999.
[16] S.Berretti, A.Del Bimbo, and P.Pala, “Indexed retrieval by shape appearance,” in Proc. VISP'00, vol. 147, no. 4, pp. 356-362, 2000.
[17] Euripides G.M. Petrakis and Evangelos Milios, “Efficient retrieval by shape content,” ICMS'99, vol. 2 , pp. 616-621, 1999.
[18] Jim Z. C. Lai and Fu-Te Hsu, “Image retrieval using semantic classification and partial match,” the 13th IPPR Conference on Computer and Vision, Graphics and Image Processing, pp. 1-6, 2000.
[19] Hui Xu and Mengyang Liao, “Cluster-Based Texture Matching for Image Retrieval,” ICIP'98, vol. 2, pp. 766-769, 1998.
[20] 戴顯權, 資料壓縮, 紳藍出版,2001.
[21] B. Mandelbrot, The Fractal Geometry of Nature, San Francisco, CA: Freeman,1982.
[22] M. F. Barnsley, Fractals Everywhere, Academic Press, San Diego, 1988.
[23] E. Jacquin, “A fractal theory of iterated markov operators with applications to digital image coding,” Ph.D. thesis, Georgia Tech., Atlanta, GA, 1989.
[24] Yuval Fisher, E. W. Jacobs, and R.D. Boss, “Iterated transformation image compression,” NOSC Thec. Rep. TR-1408, Naval Oceans Systems Center, San Diego, CA, 1991.
[25] Yuval Fisher, Fractal Image Compression : Theory and Application. Springer, New York, 1996.
[26] Arnaud E. Jacquin, “Image coding base on a fractal theory of iterated contractive image transformations,” IEEE Transactions on Image Processing, vol. 1, no. 1, pp. 18-30, 1992.
[27] G. Vines and M. H. Hayes, “Nonlinear interpolation in a one-dimensional fractal model,” in Proc. DSP'92, pp. 8.7.7-8.7.2, 1992.
[28] G. Vines and M.H. Hayes, “Nonlinear address maps in a one-dimensional fractal model,” IEEE Trans. Signal Process., 1993.
[29] G. Vine, “Signal modeling with iterated function system,” PhD thesis, Georgia Institute of Technology, Atlanat, GA, 1993.
[30] O. Maron, “Learning from Ambiguity,” Ph.D. dissertation, Massachusetts Institute of Technology, 1998.
[31] John Y. Chiang and Z. Z. Tsai, “Image based on fractal signatures,” National Computer Symposium in Taichung, 2003.
電子全文 Fulltext
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
論文使用權限 Thesis access permission:校內立即公開,校外一年後公開 off campus withheld
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available


紙本論文 Printed copies
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。
開放時間 available 已公開 available

QR Code