跳到主要內容

臺灣博碩士論文加值系統

(18.97.14.82) 您好!臺灣時間:2025/02/18 23:35
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

: 
twitterline
研究生:劉孟庭
研究生(外文):Meng-Ting Liu
論文名稱:K-均值法聚類分類技術之研究
論文名稱(外文):A study of k-means clustering
指導教授:李朱慧李朱慧引用關係
指導教授(外文):Chu-Hui Lee
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:50
中文關鍵詞:延伸K-均值法聚類分類影像切割K-均值法聚類
外文關鍵詞:Extending K-means clusteringImage segmentationClassifyK-means clustering
相關次數:
  • 被引用被引用:4
  • 點閱點閱:787
  • 評分評分:
  • 下載下載:205
  • 收藏至我的研究室書目清單書目收藏:0
聚類是一種數據分析的技術,可將各自相似的對象透過聚類方法形成不同的子集,且同一個子集中的對象都具有一些相似的屬性,常見的方法包括同空間鄰近點數、與坐標軸中最短的空間距離等,應用領域含蓋機器學習(machine learning)、資訊探勘(data mining)、模式識別(pattern recognition)、影像分析(image analysis)以及生物資訊(bioinformatics)。本研究主要分為二個部份,首先是利用K-均值法聚類(K-means clustering)方法應用於食品影像切割;再者是利用K-均值法聚類方法應用於一般數值資料,這兩個部份經修正後都有助於影像切割,在第一個部份,本研究將證明應用於食品等級分析上可切割出較佳的群組數,而第二部份,是將K-均值法聚類應用更加擴大可應用於一般數值上,而修改K-均值法聚類的部分是,改善初始群中心點於亂數選取時的錯誤,再者是對分群組數的選取,希望選取出較佳的分群組數。最後,可由實驗驗證,本研究所提出的修改後的K-均值法聚類,有助於改善原K-均值法聚類之成效。
Clustering is the assignment of a set of observations into subsets (called clusters) so the traits of observations in the same cluster are similar. According to a distance measure or numbers of nearest neighbor points, similarity measurement can be assessed. Clustering is a method of common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. There are two major parts in this thesis. The first part is K-means clustering applied in food images segmentation. The second part is K-means clustering applied in general data. In both part, we modified the K-means clustering to help the image segmentation. In the first part, we demonstrated our method can segment the food image in enough clusters for the food grading process. In the second part, we provided a heuristic approach on K-mean clustering. Initial centers would be chose in our proposed algorithm instead of randomly selection. And then we used the statistic approach to choose the suitable number of clusters. The experimental showed our proposed algorithm can help the clustering process.
目錄

中文摘要 I
ABSTRACT II
目錄 III
表目錄 V
圖目錄 VI
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
1.4 研究範圍 4
1.5 研究結構 4
第二章 文獻探討 5
2.1 分類技術 5
2.2 K-均值法聚類(K-MEANS CLUSTERING) 6
第三章 K-均值法聚類應用於影像分析之探討 8
3.1 影像切割的研究架構 8
3.2 應用於影像切割的架構流程 9
3.3 .1 影像切割演算法 10
3.3 .2 初始群中心點選取 10
3.3 .3 分群組數的選取 11
3.3 .3.1 基準組的選取 13
3.3 .3.2 較佳群組數的選取原則 13
第四章 延伸K-均值法聚類應用於一般數值資料之探討 14
4.1 延伸K-均值法聚類架構圖 14
4.2 延伸K-均值法聚類初始群中心點的選取 15
4.3 延伸K-均值法聚類分群組數的選取 16
第五章 實驗與討論 20
5.1 應用於影像切割 20
5.2 延伸K-均值法聚類初始群中心點的選取 27
5.3 延伸K-均值法聚類分群組數的選取 34
第六章 結論與未來工作 46


表目錄
表 5.1.1 各分群組數中的群中心點及群點數 24
表 5.1.2 各分群組數中的群中心點及群點數 27
表 5.2.1資料分析c3_5數據 28
表 5.2.2資料分析c3群組錯誤率比較表 30
表 5.2.3資料分析c4群組錯誤率比較表 30
表 5.2.4資料分析c5群組錯誤率比較表 31
表 5.2.5資料分析c6群組錯誤率比較表 31
表 5.2.6資料分析c7群組錯誤率比較表 32
表 5.2.7資料分析群組錯誤率總表 32
表 5.3.1資料分析c2群組分群組數選取表 34
表 5.3.2資料分析c3群組分群組數選取表 35
表 5.3.3資料分析c4群組分群組數選取表 36
表 5.3.4資料分析c5群組分群組數選取表 37
表 5.3.5資料分析c6群組分群組數選取表 38
表 5.3.6資料分析c7群組分群組數選取表 39
表 5.3.7資料分析c8群組分群組數選取表 40
表 5.3.8資料分析c9群組分群組數選取表 41
表 5.3.9資料分析c10群組分群組數選取表 42
表 5.3.10資料分析群組分群組數選取正確率總表 43

圖目錄

圖 3.1.1 影像切割架構流程圖 8
圖 3.2.1 影像切割架構流程圖 9
圖 4.1.1延伸K-均值法聚類流程圖 14
圖 5.1.1 加拿大牛肉Prime級 21
圖 5.1.2 加拿大牛肉A級 21
圖 5.1.3 美國富士蘋果 22
圖 5.1.4 使用延伸的K-均值法聚類計算後輸出的牛肉影像 C=3~ 6的輸出結果。 22
圖 5.1.5使用延伸的K-均值法聚類計算後輸出的牛肉影像C=6~8的輸出結果。 23
圖 5.1.6使用延伸的K-均值法聚類計算後輸出的蘋果影像C=3~7的輸出結果。 25
圖 5.1.7使用延伸的K-均值法聚類計算後輸出的蘋果影像C=7~10的輸出結果。 26
圖 5.2.1數值分析之c3_5分佈圖 29
圖 5.3.1資料組c10_6的分群狀況 44
圖 5.3.2資料組c10_4的分群狀況 44
參考文獻
[1]S.C. Ahalt, A.K. Krishnamurty, P. Chen, and D.E. Melton (1990), “Competitive Learning Algorithms for Vector Quantization,” Neural Networks, vol. 3, pp. 277-291.
[2]H. Akaike (1973), “Information Theory and an Extension of the Maximum Likelihood Principle,” Second Int’l Symp. Information Theory, pp. 267- 281.
[3]H. Akaike (1974), “A New Look at the Statistical Model Identfication,” IEEE Trans. Automatic Control, pp. 716-723.
[4]J. Bezdek (1981), “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenum Press, New York.
[5]H. Bozdogan (1987), “Model Selection and Akaike’s Information Criterion the General theory and its Analytical Extensions,” Psyhometrika 52, pp. 345–370.
[6]J. Breckenridge (1989), “Replicating Clustering Analysis: Method, Consistency and Validity,” Multivariate Behavioural Research.
[7]J. Brendon Woodford (2008), “Evolving Neurocomputing Systems for Horticulture Applications,” Applied Soft Computing, vol. 8, pp. 564-578.
[8]Y.M. Cheug (2005), “On Rival Penalization Controlled Competitive Learning for Clustering with Automatic Cluster Number Selection,” IEEE Trans. Knowledge Data, vol.17, pp. 1583–1588.
[9]K. Chen and Ch. Qin (2008), “Segmentation of Beef Marbling Based on Vision Threshold,” Computers and Electronics in Agriculture, vol.62, no.2, pp. 223-230.
[10]J. Fridlyand and S. Dudoit (2001), “Applications of Resampling Methods to Estimate the Number of Clusters and to Improve the Accuracy of a Clustering Method,” Technical Report 600, Statistics Dept., UC Berkeley, Sept.
[11]B. Fritzke (1994), “Growing Cell Structures—A Self-Organizing Network for Unsupervised and Supervised Learning,” Neural Networks, vol. 7, no. 9, pp. 1441-1460.
[12]R.M. Gray (1984), “Vector Quantization,” IEEE ASSP Magazine, vol. 1, pp. 4-29.
[13]P. Guo, C.L. Philip Chen, and M.R. Lye (2002), “Cluster Number Selection for a Small Set of Samples Using the Bayesian Ying-Yang Model,” IEEE Trans. Neural Networks, vol. 13, no. 3, pp. 757-763.
[14]M. Har-even and V.L. Brailovsky (1995), “Probabilistic Validation Approach for Clustering,” Pattern Recognition Letters, vol. 16, pp. 1189-1196.
[15]Ju Han and Kai-Kuang Ma (2002), “Fuzzy Color Histogram and Its Use in Color Image Retrieval,” IEEE Trans. on Image Processing, vol. 11, no. 8, pp. 944-952.
[16]T. Kohonen (1982), “Self-Organized Formation of Topologically Correct Feature Maps,” Biological Cybernetics, vol. 43, pp. 59-69.
[17]S. Khan and A. Ahmad (2004), “Cluster Centre Initialization Algorithm for K-Means Clustering,” Pattern Recognition vol. 25, pp. 1293–1302.
[18]M. Laszlo and S. Mukherjee (2007), “A Genetic Algorithm That Exchanges Neighbouring Centres for K-Means Clustering,” Pattern Recognition vol. 28, pp. 2359–2366.
[19]L.T. Law and Y.M. Cheung (2003), “Colour Image Segmentation Using Rival Penalized Controlled Competitive Learning,” 2003 Internat Joint Conference on Neural Networks (IJCNN’2003), Portland, Oregon, USA, pp. 20–24.
[20]J. Li, J. Tan and P. Shatadal (2001), “Classification of Tough and Tender Beef by Image Texture Analysis,” Meat Science, vol. 57, pp. 341-346.
[21]Y. Linde, A. Buzo, and R.M. Gray (1980), “An Algorithm for Vector Quantizer Design,” IEEE Trans. Commerce, pp. 84-95.
[22]J. Ma and B. Cao (2006), “The Mahalanobis Distance Based Rival Penalized Competitive Learning Algorithm,” Lect. Note Computer, vol. 3971, pp. 442–447.
[23]J.B. MacQueen (1967), “Some Methods for Classification and Analysis of Multivariate Observations,” 5th Berkeley Symp. on Mathematics Statist. Prob., vol. 1. University of California Press, Berkeley, pp. 281–297.
[24]T.M. Martinetz and K.J. Schulten (1991), “A ‘Neural-Gas’ Network Learns Topologies,” Artificial Neural Networks, pp. 397-402.
[25]D. Mery and F. Pedreschi (2005), “Segmentation of Colour Food Images Using a Robust Algorithm,” Journal of Food Engineering, vol. 66, no. 3, pp. 353-360.
[26]J. Oliver, R. Baxter, and C. Wallace (1996), “Unsupervised Learning Using MML,” 13th Internat Conference on Mathematics Learn, pp. 364–372.
[27]V. Olman, D. Xu, and Y. Xu (2003), “Cubic: Identification of Regulatory Binding Sites Through Data Clustering,” J. Bioinformatics and Computational Biology, vol. 1, no. 1, pp. 21-40.
[28]S.J. Redmond and C. Heneghan (2007), “A Method for Initializing the K-Means Clustering Algorithm Using kd-trees,” Pattern Recognition no.28, pp. 965–973.
[29]S.J. Roberts, R. Everson, and I. Rezek (2000), “Maximum Certainty Data Partitioning” Pattern Recognition, vol.33, pp. 833–839.
[30]Krista Rizman alik (2008), “An Efficient K''-means Clustering Algorithm,” Pattern Recognition Letters, vol. 29, no.9, pp.1385-1391.
[31]S. Rahmi, M. Zargham, A. Thakre and D. Chhillar (2004), “A Parallel Fuzzy C-Mean Algorithm for Image Segmentation,” IEEE Xplore, pp.236-237.
[32]G. Schwarz (1978) “Estimating the Dimension of a Model,” Neural Networks and Statistic. vol.6, pp. 461–464.
[33]D. Steinley and M.J. Brusco (2007), “Initialization k-means Batch Clustering: A Critical Evaluation of Several Techniques,” J. Classif. 24, pp. 99–121.
[34]G. Schwarz (1978), “Estimating the Dimension of a Model,” The Annals of Statistics, vol. 6, no. 2, pp. 461-464.
[35]A.J. Sanchez, W.Albarracin, R.Grau, C.Ricolfe and J.M.Barat (2008), “Control of Ham Salting by Using Image Segmentation,” Food Control, vol. 19, no. 2, pp.135-142.
[36]R. Tibshirani, G.Walther, D. Botstein, and P. Brown (2001), “Cluster Validation by Prediction Strength,” Technical Report, Statistics Dept., Stanford Univ.
[37]C. Wallace and D. Dowe (1999), “Minimum Message Length and Kolmogorov Complexity,” Comput.J. vol.42, pp.270–283.
[38]H.Q. Wang and D.S. Huang (2004), “A Novel Clustering Analysis Based on PCA and SOMs for Gene Expression Patterns,” Lecture Notes in Computer Science, vol. 3174, pp. 476-481.
[39]B. Wiswedel and M.R. Berthold (2007) “Fuzzy Clustering in Parallel Universes,” International Journal of Approximate Reasoning, vol. 45, no. 3, pp. 439-454.
[40]L. Xu (1997), “Bayesian Ying-Yang Machine, Clustering and Number of Clusters,” Pattern Recognition Letters, vol. 18, no. 11-13, pp. 1167-1178.
[41]L. Xu, A. Krzy_zak, and E. Oja (1993), “Rival Penalized Competitive Learning for Clustering Analysis, RBF Net, and Curve Detection,” IEEE Trans. Neural Networks, vol. 4, pp. 636-648.
[42]L. Xu (1996), “How Many Clusters?: A Ying-Yang Machine Based Theory for a Classical Open Problem in Pattern Recognition,” IEEE Int’l Conf. Neural Networks, vol. 3, pp. 1546-1551.
[43]Canada Beef Export Federation, http://www.canadabeef.com.tw/ec99/canadabeef/index_01F.asp,(2008).
[44]Canadian Food Inspection Agency, http://www.inspection.gc.ca/english/toce.shtml,(2008).
[45]U.S.APPLE, http://www.usapple.org/consumers/appleguide/grades.cfm,(2008).
[46]Food image, http://pic.n63.com/pic/?album=%2FZRshucaishuiguo&page=2&,(2008).
[47]Canada beef, http://www.cbef.com/beefquality.htm,(2008).
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊