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研究生:許雁茹
研究生(外文):Yen-Ju Hsu
論文名稱:應用於內容導向語意偵測
論文名稱(外文):Concept Detection for Content-Based Image Retrieval
指導教授:鄭錫齊鄭錫齊引用關係
指導教授(外文):Shyi-Chyi Cheng
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:69
中文關鍵詞:語意偵測智慧型內容導向影像檢索視覺物件影像切割語意學習
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本論文提出一新的語意學習方法,利用一群已標記的影像產生可能的語意訊息來偵訊圖片中的主要語意物件,並據以應用到內容導向影像檢索的應用上。在本方法中,將資料庫的影像分為兩類-已標示(labeled)語意群與未標示(unlabeled)群之影像,對每一個已標記的影像我們設計一基於低階特徵之語意學習模型。資料庫裡的所有影像都會先經過影像切割方法切割成多個區塊,進而抽取代表這些區塊的三種不同形態的低階視覺特徵(顏色、形狀、紋理),根據這些低階視覺特徵的統計資料建造出語意偵測模型來預測分析隱藏在資料庫裡的語意資訊。由於人類對於影像中所包含的語意特徵是很主觀的,所以使用具人工標記的影像的低階特徵所建立的統計模型來進行影像做註解常有模糊不清的問題。為解決這個問題,本論文提出一區域權重估測演算法,選取具最大的語意資訊之重要區域,抽取其特徵後,進行隱含語意義涵之區域式內容導向影像檢索。在檢索的過程中,只有重要區域的特徵才用來當作計算影像間語意距離的特徵向量,此語意學習架構對內容導向影像檢索(Content-Base Image Retrieval , CBIR)系統提供了一個連結高階語意概念與低階影像特徵的橋樑。實驗結果顯示我們所提出的方法與其他相似的語意學習方法,在效能上有更好的表現。
In this thesis, a new semantic learning method to detect semantic region for image retrieval from a given amount of labeling effort is proposed. In our approach, the database images are classified into two classes –the labeled class and the unlabeled class. Form images in the labeled class, we construct a concept detection to detect the important regions in each image based on the statistical information of a semantic class. All the images in the database are segmented into multiple disjoint regions, each of them is represented by three type of low-level visual features ( i.e. color, shape, and texture). With this representation a region weighting model based on the statistical information of low-level visual features is predicted to analyze semantic concepts hidden in the database. One key obstacle in applying statistical methods to discover the hidden semantic concepts for annotating images in the amount of manually-labeled images is normally insufficient. For images that have not been annotated, the learning algorithm estimates their important regions whose low-level features are then extracted to retrieve semantic all similar image s form the test data base. Experimental results show that the performance of the proposed method is excellent as compared with that of simulated traditional content-based image retrieval.
摘要 I
Abstract II
誌謝 III
目錄 IV
壹、 緒論 1
1.1 研究背景 2
1.2 研究目的 5
1.3 論文架構 6
貳、 文獻探討 7
2.1 基於內容導向之影像檢索系統簡介 7
2.1.1 影像切割 8
2.1.2 低階特徵與影像檢索系統 10
2.2 智慧型影像檢索系統 15
2.2.1 機器學習(Machine learning) 16
2.2.2 使用者回饋(Relevance feedback,RF) 19
2.2.3 建立語意樣板(Semantic template,ST) 21
2.3 觀念檢測器 22
2.4 影像檢索評估系統簡介(TRECVID) 23
參、 語意偵測演算法 25
3.1 語意學習機制 27
3.2 計算各區域之權重的方法 30
3.3 語意偵測 33
肆、 智慧型內容導向影像檢索方法設計 36
4.1 系統簡介 36
4.2 應用語意偵測於RBIR系統方法設計 40
4.3 效能評估與比較 42
伍、 實驗結果 44
5.1 程式介面 44
5.2 影像切割 46
5.3 語意偵測實驗設計 48
5.3.1 實驗一 48
5.3.2 實驗二 57
5.3.3 實驗三 59
5.4 實驗分析與改進方向 60
陸、 結論及未來展望 63
柒、 參考文獻 65
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