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研究生: 陳建宏
Jianhung Chen
論文名稱: 以色塊屬性關聯規則建立影像分類決策之研究
An Image Classification Strategy based on Association Rules of Color-blocks
指導教授: 柯佳伶
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2001
畢業學年度: 89
語文別: 中文
論文頁數: 65
中文關鍵詞: 影像分類多決策樹色塊關聯規則精簡規則
英文關鍵詞: image classification, multi-decision tree, color block, association, pruning rules
論文種類: 學術論文
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  • 近來影像分類的研究,大部份是針對特定的應用領域訂定全域式的影像特徵,因此不易應用於不同領域的影像主題,且全域式的影像特徵較不能顯示出個別物件與空間位置的影像特徵。本論文提出以色塊屬性關聯規則作為影像特徵,我們首先將影像轉換至HSV色空間上並量化,以色塊指標串列輔助取出色塊及色塊屬性的計算,接著再建構二元關聯次數累計表以快速計算出一張影像中的色塊屬性關聯規則之支持度與確信度。本論文提出動態多決策樹演算法來挑選出對區分影像類別具重要性的色塊屬性關聯規則,進而建立影像分類決策規則;亦提出影像分類決策規則的精簡方法,可有效降低分類規則的數量,且不明顯影響分類正確率。我們亦將分類方法擴增為模糊型式,可接受具模糊類別的訓練影像並產生模糊分類結果。在我們的實驗中顯示,本論文所提出之影像分類方法分類的正確率明顯優於C4.5與模糊決策樹,並對於各種不同種類的影像領域皆能達到一定程度的分類正確率。

    Most previous works on image classification which are purposed for specific image domain, extract global image properties to be feature of an image. However, the global image properties can't represent objects and spatial features well. In this thesis, a kind of object-based image feature is designed, called Block Attribute Association Rules (BAAR), which indicates the relationship among locations and sizes of color blocks. First, the color domain of an image is transformed to HSV color space and quantized to be 148 colors. After that, color blocks and their content attributes are extracted efficiently by applying Block List. The Binary Relationship Counting Table (BRCT) is designed for computing the supports and confidences of BAARs efficently. Moreover, Dynamic Multi-Decision Tree (DMDT) algorithm is proposed for deriving classification rules, and a pruning algorithm is provided to reduce the number of classification rules. The proposed strateies are also extended to perform fuzzy classification. According to the experiment results, it shows that the classification accuracy of proposed classification methods is superior than C4.5 and fuzzy decision tree, and the proposed strateies are applicable on various image domains well.

    第壹章 緒論 第貳章 名詞定義與問題描述 第一節 基本定義 第二節 問題描述 第三節 色塊屬性關聯規則分類方法 第參章 色塊屬性關聯規則抽取方法 第一節 色塊屬性取出方法 第二節 色塊屬性資料量化與轉換 第三節 色塊屬性關聯規則特徵 第肆章 動態多決策樹分類方法 第一節 建立決策樹訓練資料 第二節 動態多決策樹演算法 第三節 多決策樹分類方法 第四節 分類規則精簡 第五節 動態模糊多決策樹 第伍章 演算法效能評估 第一節 分類正確率測試 第二節 將模糊分類結果應用於影像查詢 第陸章 結論 參考文獻

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