近年來，隨著資訊科技的發展與進步，影像處理的需求日漸增大，越來越多的媒體資訊，以數位化的形式在日常生活中使用，現今，不管是要取得或產生數位影像都是非常容易的事，但是要在龐大的影像資料庫中快速又精確地找到符合需求的影像，是一個值得深入研究的議題。
一般來說，以內容為基礎的影像檢索(Content-Based Image Retrieval, CBIR)技術，主要由以下兩個步驟來進行影像檢索 : 首先針對每張影像做特徵提取的計算，並儲存在特徵資料庫中。第二步驟則是藉由提供查詢影像(Query Image)給系統，經過相同的特徵計算並與資料庫中的每一張影像特徵相比較，找出最相似的影像回傳給使用者。
本研究提出一個顯著影像特徵檢索技術SMFD(Salient Point and Multi- Features Descriptor Based Image Retrieval Method)，首先透過哈里斯角點偵測(Harris Corner Detector)進行特徵點偵測以找出影像的特徵區域，再根據特徵區域中的彩度、明度計算其顏色平均值、變異數、顏色出現的次數及顏色聚集程度等特徵向量值，來抵抗影像中存在的幾何攻擊，透過特徵區域的匹配，計算其相似度並將最相似之影像回傳給使用者。在實驗結果，已測試不同類型之動物資料庫和Caltech 101人臉資料庫，對於影像檢索的精確率和召回率都有不錯的實驗結果，因此本方法可有效檢索相似之影像。

In recent years, with the development of information technology, There have an increasing demand for image processing. More and more media information with digital form is used in our everyday life. Now, to acquire or to generate digital images are very easy, but how to quickly and accurately meet the requirements to find the image in large image databases, is a intensive topic for further studies.
In general, content-based image retrieval technology mainly includes the following two steps for image retrieval: first is the extracting features for each image and to store them in the feature database. The second step is to provide query by image to the system; to calculate similarity by the characteristics with the database features for each image and to identify the most similar images which are return back to the user.
This research presents a significant image feature retrieval technology (Salient Point and Multi-features Descriptor based Image Retrieval Method, SMFD), The feature points are first detected by Harris corner to identify the features of image, and then the characteristics of the same color are formed to resist geometric attacks. The characteristics are the saturation, brightness, the average and, variance, the number of occurrences and the degree of the same color gathered. By matching feature regions through calculating the similarity, the similar images are return to the user. In the experimental results, different types of animals and the Caltech 101 face databases have been tested. As a result, the image retrieval precision and recall results are good, so this method can retrieve similar images of a image databases effectively.

目錄
第一章 緒論..............................................1
1.1 研究背景.............................................1
1.2 研究動機與目.........................................6
1.3 論文架構.............................................7
第二章 相關文獻..........................................8
2.1 數位影像簡介.........................................8
2.2 影像檢索系統.........................................9
2.3 特徵點偵測法.........................................12
2.3.1 哈里斯角點偵測.....................................12
2.4 單一特徵影像檢索技術.................................17
2.4.1 Swain等學者提出之顏色直方圖影像查詢系統............17
2.4.2 Huang等學者提出之顏色相關直方圖....................18
2.5 多重特徵影像檢索技術.................................19
2.5.1 Qiu學者提出之頻率層顏色索引技術....................19
2.5.2 Tsai學者提出之多特徵維度之影像檢索法...............20
2.6 不變顯著區域影像檢索技術.............................21
2.6.1 Shao等學者提出之顯著區域基於特定對象檢索法.........21
2.6.2 Ledwich等學者提出之降低SIFT在室內定位影像檢索法....22
第三章 植基於顯著影像特徵之影像檢索技術之研究............25
3.1顯著特徵提取..........................................26
3.2顯著點多特徵向量值之建立..............................27
3.2.1 HSV色彩空間........................................27
3.2.2 HSV多特徵值向量擷取頻率特徵........................29
3.3 相似度計算...........................................32
第四章 實驗結果..........................................34
4.1 實驗用資料庫介紹.....................................35
4.2 影像檢索評估和實驗...................................38
4.2.1 評估方式...........................................38
4.2.2 實驗結果...........................................38
第五章 結論..............................................45
5.1 結論.................................................45

圖目錄
圖 1 QBE概念圖...........................................5
圖 2 影像的定義..........................................9
圖 3 搜尋視窗在影像中移動示意圖..........................12
圖 4 M矩陣分解之特徵值橢圓示意圖.........................15
圖 5 哈里斯角點偵測之特徵值定義..........................16
圖 6 顏色相關示意圖......................................19
圖 7 系統架構............................................25
圖 8 運用哈里斯角點檢測..................................27
圖 9 HSV色彩空間座標模型.................................28
圖 10 影像特徵區域多特徵向量值區域示意圖.................30
圖 11 相對亮度不等距量化(其中s=7 ).......................31
圖 12 特徵區域中像素對鄰近像素聚集示意圖.................31
圖 13 Caltech 101資料庫中人臉資料庫......................34
圖 14 從Google搜尋引擎尋找的30種動物類型影像範例.........35
圖 15 從Google搜尋引擎尋找的影像結果 (a)貴賓狗 (b)海豚...36
圖 16 Caltech 101資料庫中22種人臉資料庫實驗影像範例......37
圖 17 Caltech 101資料庫中其中3類人臉資料庫的實驗影像範例.38
圖 18 動物類型貴賓狗PR值比較 (a)貴賓狗 (b)比較圖.........39
圖 19 動物類型貓PR值比較 (a)貓 (b)比較圖.................39
圖 20 動物類型北極熊PR值比較 (a)北極熊 (b)比較圖.........40
圖 21 動物類型海豚PR值比較 (a)海豚 (b)比較圖.............40
圖 22 動物類型班馬PR值比較 (a)班馬 (b)比較圖.............40
圖 23 動物類型花豹PR值比較 (a)花豹 (b)比較圖.............40
圖 24 動物類型綠鸚鵡PR值比較 (a)綠鸚鵡 (b)比較圖.........41
圖 25 動物類型刺蝟PR值比較 (a)刺蝟 (b)比較圖.............41
圖 26 30類動物總平均PR值曲線比較.........................41
圖 27 人臉類型第一類PR值比較 (a)第一類人臉 (b)比較圖.....42
圖 28 人臉類型第二類PR值比較 (a)第二類人臉 (b)比較圖.....42
圖 29 人臉類型第三類PR值比較 (a)第三類人臉 (b)比較圖.....43
圖 30 人臉類型第四類PR值比較 (a)第四類人臉 (b)比較圖.....43
圖 31 人臉類型第五類PR值比較 (a)第五類人臉 (b)比較圖.....43
圖 32 人臉類型第六類PR值比較 (a)第六類人臉 (b)比較圖.....43
圖 33 人臉類型第七類PR值比較 (a)第七類人臉 (b)比較圖.....44
圖 34 人臉類型第八類PR值比較 (a)第八類人臉 (b)比較圖.....44
圖 35 22類人臉總平均PR值曲線比較.........................44

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