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研究生:安寶楹
研究生(外文):Pao-Ying An
論文名稱:以循序特徵關聯方式探勘影像分類規則之研究
論文名稱(外文):Mining Associative Sequential Rules for Image Classification
指導教授:柯佳伶柯佳伶引用關係
指導教授(外文):J. L. Koh
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:資訊教育研究所
學門:教育學門
學類:專業科目教育學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:58
中文關鍵詞:循序探勘影像分類
外文關鍵詞:Sequential Pattern MiningImage classification
相關次數:
  • 被引用被引用:1
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
由於影像資料的大量普及,為使影像資料能自動進行分類索引,以使影像資料儲存更有組織性以方便搜尋擷取,本論文提出使用循序特徵關聯規則探勘方式來找出影像分類規則的方法。
本論文提出以影像特徵項序列來代表一張影像資料的特徵,先將影像轉換至HSI色空間,經過色彩量化之後取出色塊當作影像物件,以各不同顏色值的色塊做為一種影像特徵項,再依色塊的位置屬性值將影像特徵項進行漸增排序,以保留各色塊物件間位置的相對關係之資訊,最後所得之影像特徵項序列即代表該影像資料的特徵。
接下來對這些影像特徵項序列進行探勘,找出常出現特徵項序列,進而找出特徵項序列和影像類別間之關聯,來建立影像分類規則。我們提出以位元運算方式為基礎的循序特徵探勘演算法,搭配所設計的位元索引表及出現索引表資料結構,可有效率地找出常出現特徵項序列,並產生其對應的分類規則。
在分類方法的部分,我們將同一類別的每個分類規則依其信賴度進行標準化當作該分類規則對該類別的分類貢獻度。在影像分類判斷時以該影像在各類別符合之多個分類規則的分類貢獻度分別做加總,值最高者即判定為該影像所屬類別。
最後,我們以自然影像及動物影像來進行本方法分類正確率的評估,證實了本論文所提出的分類方法對於不同類型的影像皆可達到平均92%以上的分類正確率,並比其他影像分類方法達到更好的分類正確率。
In this thesis, a new image classification method based on mining associative sequential rules is proposed.
First, the colour blocks in an image is extracted. Moreover, the attribute values of the colour blocks are recorded, including the area, x-position, y-position of the color block and so on.
A colour block with a specific colour is defined as an image feature term.The extracted colour blocks are sorted according to a colour attribute to form a sequence of image feature terms, which is the data used to represent the characteristic of an image.
Moreover, an efficient sequential pattern mining algorithm is provided. The frequent sequential patterns are mined from the sequences of image feature terms extracted from training images to derive associative classification rules. The data structures “bits index table” and “appearing index table” are designed to assist mining frequent sequential patterns and classification rules quickly.
Finally, the judgement method of classification is designed based on multiple classification rules instead of one single rule.
The experiments are performed on natural images and animal images obtained from Corel Gallery CD. The results show that the average accurate rate of image classification, achieved by the method proposed in this thesis, is above 92%. In addition, the performance of accurate rate of our method is better than the related works.
第一章 緒論 1
1.1背景與研究動機 1
1.2 論文架構 7
第二章 影像特徵擷取方法 8
2.1 影像特徵轉換 8
2.2 影像特徵項序列 15
第三章 常出現特徵項序列探勘 19
3.1位元索引表(Bits Index Table) 19
3.2出現索引表(Appear Sequence Index Table) 21
3.3常出現特徵項序列的探勘方法 23
第四章 影像分類方法 38
4.1建立分類規則 38
4.2分類方法 45
第五章 演算法效能評估 47
5.1 分類正確率測試方法 47
5.2 實驗結果 49
第六章 結論 56
參考文獻 57
[1] L.Breiman, J.H. Friedman, R.A Olshen and C. J. Stone, “Classification and Regression Trees,” Belmont, CA: Wadsworth International Group, 1984.
[2] S. Belongie, C. Carson, H. Greenspan, and J. Malik, “Recognition of images in large databases using a learning framework,” Technical Report TR 97-939,U.C. Berkeley, CS Division, 1997.
[3] S.D. Bona and O. Salvetti, “An Enhanced Neural System for Biomedical Image Classification,” in Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation, 2000.
[4] C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Region-based image querying,” in IEEE Workshop on Content-Based Access of Image and Video Libraries,1997.
[5] G.. Dong, X. Zhang, L. Wong and J. Li.,“CAEP: Classification by Aggregating Emerging Patterns,” DS-99: Proceedings of Second International Conference on Discovery Science, Lecture Notes in Computer Science, Vol. 1721 Springer ,1999.
[6] D. Forsyth, J. Malik, M. Fleck, H. Greenspan, T. Leung,S. Belongie, C. Carson, and C. Bregler, “Finding pictures of objects in large collections of images,” Technical report, U.C. Berkeley, CS Division, 1997.
[7]R.C Gonzalez and R. E. Woods, Digitial Image Processing, Addison Wesley Longman, Inc., 1992.
[8]J. L. Koh and W.D.C Yu, “Efficient Feature Mining in Music Objects,” Lecture Notes in Computer Science: DEXA’ 01: Database and Expert Systems Applications, Springer-Verlag, 2001
[9] B. Liu, W. Hsu Y. Ma , “Integrating Classification and Association Rule Mining,” in Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), New York, 1998, pp. 80-86.
[10]N. Lesh, M. J. Zaki, M. Ogihara, “Scalable Feature Mining for Sequential Data,” IEEE Intelligent Systems, 2000
[11] W. Li, J. Han, and J. Pei. “CMAR: Accurate and Efficient Classification Based on Multiple Class-association Rules”, Proc. The 2001 IEEE International Conference on Data Mining (ICDM''01), 2001.
[12] D. Meretakis and B. Wuthrich, “Classification as Mining and Use of Labeled Itemsets,” ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Philadelphia, USA, May 30, 1999
[13] J. R. Smith and C.-S. Li, “Image classification and querying using composite region templates, ” Journal of Computer Vision and Image Understanding, 1999.
[14]M.S. Chen, J. Han, and P.S. Yu, “Data Mining: An Overview from a Database Persective,” IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, 1996
[15]J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu, “PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth,” in Proceedings of the 17th International Conference on Data Engineering, 2001
[16]陳建宏, 柯佳伶, “以色塊屬性關聯規則建立影像分類決策, ” in Proceedings of 2001 National Computer Symposium, 2001.
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