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系統識別號 U0026-0812200911380447
論文名稱(中文) 整合代表性與關聯性分類子的影像分類法則
論文名稱(英文) Classify By Representative Or Associations (CBROA) : A Hybrid Approach for Image Classification
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 93
學期 2
出版年 94
研究生(中文) 李宗杰
研究生(英文) Chon-Jei Lee
電子信箱 mark@dmlab.csie.ncku.edu.tw
學號 p7692149
學位類別 碩士
語文別 中文
論文頁數 62頁
口試委員 口試委員-李強
口試委員-洪宗貝
口試委員-林文揚
指導教授-曾新穆
中文關鍵字 關聯法則  決策樹  影像分類  資料探勘 
英文關鍵字 Association Rules  Data Mining  Image Classification  Decision Tree 
學科別分類
中文摘要   在多媒體資料研究分析領域中,由於影像分類的應用層面甚廣,故一直以來是熱門的研究。我們觀察出要了解影像所表達的特性(或進而演化成影像註解)可以由兩方面來著手:一、由影像內某一主要物件的意涵為基礎;二、由影像內各物件之間的關係性找出其所屬類別。這兩種分類思考模式在日常生活中層出不窮,然而,目前許多研究可以解決其中一種類型的影像分類,而在另一種類型的影像分類上面則較薄弱,無法兼顧。本研究提出一名為 “CBROA” (Classify By Representative Or Associations,基於代表性或關聯性的分類法則)的整合型影像分類法則,同時將這兩種影像特性考慮進去。CBROA 整合了決策樹以及關聯性探勘法則的特性,並搭配虛擬意涵架構完成影像分類。實驗結果顯示在多個類別的影像資料中,CBROA 的分類準確比 SVM 高 17.2%,比 C4.5 高 14.6%。
英文摘要  Image classification has been an attractive research issue in multimedia content analysis due to the wide applications. In this research, we observe that images can be classified (or annotated) in two ways: i) Classify by some main object, ii) Classify by multiple objects with their relations. These two types of images usually exist concurrently in real-life image databases. Although a number of image classification methods have been proposed, they can only handle one certain type of images well and fail to deal with both types of images correctly at the same time. In this research, we proposed a hybrid image classification method, namely “CBROA” (Classify By Representative Or Associations), that can effectively classify both types of images at the same time. CBROA integrates the decision tree and association mining methods in an adaptive manner with construction of a virtual semantic ontology. Experimental results show that CBROA outperforms SVM and C4.5 in terms of classification accuracy for classifying mixed types of images by 17.2% and 14.6% respectively.
論文目次 第一章 導論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 問題描述 3
1.4 研究方法 4
1.5 論文架構 6
第二章 文獻探討 7
2.1 以內容為基礎的影像搜尋系統 7
2.2 影像低階特徵 8
2.3 分類問題相關研究及文獻 10
2.3.1 隱藏式馬可夫模型 10
2.3.2 kNN 11
2.3.3 SVM 11
2.3.4 決策樹類方法 12
2.3.4.1 屬性選擇 12
2.3.4.2 樹的建立 14
2.3.5 關聯規則分類法 14
2.4 影像分割 15
第三章 CBROA 判斷法則 22
3.1 方法架構 23
3.2 影像低階特徵值擷取 24
3.3 訓練階段 26
3.3.1 代表性物件分類子之制置(Classify By Representative) 26
3.3.2 物件關聯性分類子制置(CBA) 28
3.3.2.1 影像虛擬碼轉換及虛擬意涵架構 29
3.3.2.2 關聯法則建立 30
3.4 CBROA(CBR vs. CBA) 36
第四章 實驗分析 39
4.1 實驗基本資料設定 39
4.2 實驗規劃 42
4.2.1 Normalized Cut 參數設定 42
4.2.2 CBA 的支持度門檻及交易記錄長度設定 43
4.3 實驗結果 45
4.4 實驗總結 53
第五章 結論 56
第六章 參考文獻 58
作者自述 62
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