跳到主要內容

臺灣博碩士論文加值系統

(34.204.169.230) 您好!臺灣時間:2024/02/21 22:43
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:賴世金
研究生(外文):Shih-jin Lai
論文名稱:影像註解之資訊探勘方法
論文名稱(外文):Information Mining of Image Annotation
指導教授:蔣依吾蔣依吾引用關係
指導教授(外文):John Y. Chiang
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊工程學系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:96
中文關鍵詞:資訊探勘影像註解碎形
外文關鍵詞:fractalimage annotationimage information mining
相關次數:
  • 被引用被引用:0
  • 點閱點閱:170
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
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。
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.
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
[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.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文