隨著數位影像處理工具的日益普及，產生了大量豊富且多樣的影像資料，如何有效的從龐大的影像資料庫中檢索出使用者所要的影像，成為影像檢索上重要的課題。早期的影像檢索是透過影像的文字說明，但影像資料的描述，若用傳統人工輸入方式建置，不但較為主觀，而且需花費太多的時間、金錢和人力去管理，因此陸續有學者提出以影像本身的內容，如顏色、紋理、物件之形狀、物件之空間關係等等，作為影像檢索之基礎。
本論文針對影像內容中物件之空間關係，提出以物件間作用力分佈圖為基礎之數個乏晰空間關係特徵值，並將這些值作為相似比對之依據。這種方法與過去直接利用分佈圖配對的方式相較，優點在於(1)計算效率上較快速;(2)利用擷取出之空間關係特徵值建立索引，可在檢索相似配對時，節省與資料庫影像比對的時間;(3)這些特徵值具有語意上認知之合理性。本論文以實驗說明我們所建置之方向、包圍、遠近等乏晰空間關係，能較完整展現影像物件間空間關係之差異性，未來應可用於語意式影像檢索系統中。

With the popularity of digital image generation and processing tools, huge miscellaneous rich image data have been produced. How to effectively retrieve images from the huge image databases has become an important subject. In the early stage, image retrieval was achieved by matching keywords with image description text. However, the manual input of image description is not only too subjective, but also spends a lot of time, money and manpower. Thus, several researchers proposed successively retrieval methods based on the image content, such as color, texture, shapes of objects, spatial relationships of objects, etc. 
 
To obtain a better matching of spatial relationships, we propose several fuzzy spatial relationship characteristic values based on the force histograms among the objects in the images. These values are further used to compute the similarity of two images. This method, compared with direct histogram matching, has the following advantages: (1) It has better computational efficiency; (2) It could precompute the characteristic values of the spatial relationships and and store them in the database, which tremendously saves time in retrieving similar images; (3) These characteristic values are associated with more human-reasonable semantic meanings. Lastly, we demonstrate the use of fuzzy directional, surrounding and distance spatial relationships in image retrieval. The results illustrate that these fuzzy spatial relationships can extract the difference of the spatial relationship among the images more completely. We hope this system could be applied to semantic retrieval of the images in the future.

第一章 緒論	1
1.1 研究背景與動機	1
1.2 研究目的	4
1.3 論文架構	5
第二章 相關研究及文獻探討	6
2.1 利用最小邊界矩形之影像相似比對	7
2.1.1 直接法相似比對	7
2.1.2 間接法相似比對	8
2.2 乏晰空間關係之相關研究	10
2.2.1 乏晰理論於空間關係之應用	11
2.2.2 相容法(Compatibility Method)	13
2.2.3 彙集法(Aggregation Method)	15
第三章 物件間乏晰空間關係之擷取	18
3.1 物件間空間關係之作用力分佈圖	19
3.2 空間關係特徵之擷取	23
3.2.1 使用上 、下 、左、右之影像檢索問題探討	23
3.2.2 包圍特徵之擷取	26
3.2.3 遠近(FAR/NEAR)特徵之擷取	29
3.3 計算相似度之方法	31
第四章 實驗分析	33
4.1系統架構	36
4.2 實驗說明與分析	37
4.2.1 實驗一(特例影像)說明	37
4.2.2 實驗一之結果分析:	41
4.2.3 實驗二說明	44
4.2.4 實驗二之結果分析:	48
第五章 結論	52
參考文獻	54


圖目錄

圖1: (A) A物件較大之影像，(B) A物件較小之影像。	7
圖2: 2D-STRING範例。	9
圖3:“A在B的左方”關係產生混淆。	10
圖4: A、B兩點間之空間關係。	12
圖5: 物件A、B上之任兩點AII、B 連線與水平線所形成之夾角。	13
圖6: 四個方向關係之乏晰集合，其 歸屬函式是利用 、 。	13
圖7: (A)乏晰集合R、 之歸屬函式， (B)R、 所形成之相容乏晰集合。	15
圖8: 四個方向關係之乏晰集合。	16
圖9: (A)點對點關係，(B)切段對切段關係。	19
圖10: (A)A在B的 方向作用力分析，(B) 切過 物件A所形成之區段A( )(同: )。	20
圖11: (A) 物件A對B之作用力分佈圖FAB，(B) A在B 的 方向作用力份佈圖FAB+ ， 。	21
圖12: A在B之 方向之有效作用 力分佈， 。	24
圖13: (A)(B)不同空間分佈，卻有相同HE值，(C)解決 偏斜影響之乏晰歸屬函式 。	25
圖14: (A)測試影像T1，(B)測試影像T2，(C) T1作 用力分佈圖，(D) T2作用力分佈圖。	26
圖15: (A) 值與H(Θ)之切線交集集合為Q0I、Q1I、Q2I、Q3I， (B)若 < ,則 ，(C) (Θ)之極座標表示，其缺角 Z0>Z1>Z2>Z3。	28
圖16: (A)(B)中粗線為方向上之相對位置，虛線 為重心之相對位置。	30
圖17: 可調整關係權重比之查詢介面。	34
圖18: 兩影像間差異性比較介面。	35
圖19: 系統架構圖。	36
圖20: 第一群影像。	39
圖21: 第二群影像。	40
圖22: 第三群影像。	41
圖23: (A)查詢影像Q1，(B)查詢影像Q2。	44
圖24: 三十張資料庫影像。	47

 
表目錄

表1: 第一群影像中物件間之乏晰空間關係值。	42
表2: 第二群影像中物件間之乏晰空間關係值。	43
表3: 第三群影像中物件間之乏晰空間關係值。	43
表4: 查詢影像Q1對圖24中資料庫影像之各項差異值。	50
表5: 查詢影像Q2對圖24中資料庫影像之各項差異值。	51

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