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:226 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.
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