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研究生: 李純琇
論文名稱: 以循序特徵決策樹探勘影像分類規則
Mining Image Classification Rules based on Decision Tree of Sequential Patterns
指導教授: 柯佳伶
學位類別: 碩士
Master
系所名稱: 資訊教育研究所
Graduate Institute of Information and Computer Education
論文出版年: 2003
畢業學年度: 91
語文別: 中文
論文頁數: 60
中文關鍵詞: 影像分類決策樹循序樣式資料探勘
英文關鍵詞: image classification, decision tree, sequential patterns, data mining
論文種類: 學術論文
相關次數: 點閱:211下載:13
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  • 由於網際網路上的影像資料日益增多,為使影像資料建立分類目錄時,能有效進行影像自動分類,本論文提出採用影像循序特徵建立決策樹以產生分類規則的影像自動分類方法。首先在影像特徵擷取部份,將影像的顏色從RGB色空間轉換至HSI色空間,經由色彩量化取出色塊當作影像物件,以色塊不同顏色值做為影像特徵項,再依其色塊的位置屬性值大小排序產生影像特徵項序列。接下來利用循序探勘法找出所有常見特徵項序列、最大常見特徵項序列、及最長常見特徵項序列,將這些特徵項序列定為影像的分類屬性,並依影像是否包含特徵項序列來設定該屬性值為0或1,最後套用C4.5建立決策樹並產生影像分類規則。為了讓系統能漸進式學習,當系統分類結果由人為判斷是錯誤時,分類錯誤影像的特徵可再加入訓練樣本,以期增進系統的分類正確率。為了在重新探勘分類規則時較有效率,系統除保留原訓練資料集的常見特徵項序列的資訊外並保留負邊界樣式,以漸進式探勘方式找出新分類屬性,以節省再次重新掃描所有影像特徵項序列的時間。實驗評估結果顯示,本論文所提出之影像分類方法於不同類型之影像皆可達到不錯的分類效果。此外,比較另一個影像分類方法,本方法達到較好的分類正確率,且在分類規則比對時需較少的比較次數。

    In this thesis, a method of image classifications is proposed. This approach is designed based on constructing decision trees for sequential patterns. First, the color space of images is transferred from RGB to HSI. After performing quantization on the color space, color blocks in an image are extracted and blocks with the same color are assigned the same identifiers of feature terms. According to y-positions of color blocks, blocks are sorted to form a sequence of feature terms in order to represent features of an image. Frequent sequential patterns, mined from the sequences of image feature terms extracted from training images, are used to be the attributes for classification. Finally, according to the selected attributes, a decision tree is constructed by performing C4.5 algorithm to find the classification rules. Moreover, in order to improve the accurate rate of classification, new images which are assigned the wrong categories by the system can be inserted into training set to re-train the classification rules. For achieving more efficient performance when performing re-training, the concept of incremental mining is applied in the system to preserve the information of frequent sequential patterns and negative borders in the previous training images. Such that it prevents re-scanning the whole training data set to select the new classification attributes. The experiment results show that the accurate rates of the proposed method is good for various kinds of image. Furthermore, by comparing with another related work, our method has better accurate rate and has less numbers of comparisons when searching classification rules.

    附表目錄 v 附圖目錄 vii 第一章 緒論 1 第一節 研究動機 1 第二節 論文方法 3 第三節 論文章節 3 第二章 相關研究及系統架構 4 第一節 相關研究 4 第二節 系統架構 8 第三章 影像特徵項序列探勘 11 第一節 影像特徵擷取 11 第二節 循序特徵項序列 13 第四章 漸進式決策樹分類方法 18 第一節 建立決策樹訓練資料 18 第二節 產生決策樹 20 第三節 比對方法 29 第四節 分類錯誤之影像再訓練 30 第五章 演算法效能評估 32 第一節 分類正確率測試 32 第二節 實驗結果 38 第六章 結論 57 參考文獻 58 附表目錄 表3.1 色塊屬性表 15 表3.2 色塊轉為影像特徵項表 15 表3.3 影像特徵資料表 16 表4.1 影像屬性表 19 表4.2 屬性對應GainRatio表(1) 22 表4.3 屬性對應GainRatio表(2) 24 表4.4 屬性對應GainRatio表(3) 26 表4.4 決策樹資料表 29 表5.1 影像資料集 33 表5.2 最小支持度對於正確率之影響 39 表5.3 最小支持度對於比對次數之影響 40 表5.4 資料集1混淆矩陣 41 表5.5 最小支持度對於正確率之影響 43 表5.6 最小支持度對於比對次數之影響 43 表5.7 資料集2混淆矩陣 45 表5.8 影像分類方法二之漸進式分類正確率表 47 表5.9 影像分類方法三之漸進式分類正確率表 47 表5.10 影像分類方法四之漸進式分類正確率表 48 表5.11 影像分類方法二之漸進式分類正確率表 49 表5.12 影像分類方法三之漸進式分類正確率表 49 表5.13 影像分類方法四之漸進式分類正確率表 50 表5.14 影像分類方法二之漸進式分類正確率表 51 表5.15 影像分類方法二之漸進式分類正確率表 51 表5.16 影像分類方法四之漸進式分類正確率表 52 表5.17 影像分類方法二之漸進式分類正確率表 53 表5.18 影像分類方法三之漸進式分類正確率表 53 表5.19 影像分類方法四之漸進式分類正確率表 54 表5.20 最小支持度對於正確率之影響 55 附圖目錄 圖2.1系統架構圖 10 圖3.1 影像色塊示意圖 13 圖3.2 樹狀結構 17 圖4.1 決策樹(1) 23 圖4.2 決策樹(2) 25 圖4.3 決策樹(3) 27 圖4.4 決策樹 28 圖5.1 影像表 38 圖5.2 資料集1對分類之正確率 40 圖5.3 資料集1之測試影像集分類的比對次數 40 圖5.4 資料集2對分類之正確率 43 圖5.5 資料集2之測試影像集分類的比對次數 44 圖5.6 影像分類方法二之漸進式分類正確率圖 47 圖5.7 影像分類方法三之漸進式分類正確率圖 47 圖5.8 影像分類方法四之漸進式分類正確率圖 48 圖5.9 影像分類方法二之漸進式分類正確率圖 49 圖5.10 影像分類方法三之漸進式分類正確率圖 49 圖5.11 影像分類方法四之漸進式分類正確率圖 50 圖5.12 影像分類方法二之漸進式分類正確率圖 51 圖5.13 影像分類方法二之漸進式分類正確率圖 52 圖5.14 影像分類方法四之漸進式分類正確率圖 52 圖5.15 影像分類方法二之漸進式分類正確率圖 53 圖5.16 影像分類方法三之漸進式分類正確率圖 54 圖5.17 影像分類方法四之漸進式分類正確率圖 54 圖5.18 資料集5對分類之正確率 55

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