在網際網路發達的現今社會，圖像檢索系統已經非常的普遍，所以如何建立一個有效率以及準確性高的圖像檢索系統已經變成了最需要考慮的條件。當我們想要設計一個以內容為基礎的圖像檢索系統時，會遇到一個困難，就是該選擇圖像的何種低階特徵來當成比對的依據，往往都很難兩全其美，只能適用某一部份物件類型，對於其他的物件種類卻不能適用。

    我們的系統就是想要解決這方面的問題，利用同一種類圖像的大量訓練資料彼此間的相似度，藉由調整物件低階特徵的比例，可以使得該物件種類的辨識率達到最高，而不同種類的物件有其最適合的低階特徵組合。經過實驗的結果之後，我們建立了圖像語意以及低階特徵的關連性。

    在系統實作的過程中，我們利用圖像資料庫來取得實驗用之圖像，並從中選擇想要建立模組的圖像類型。利用大量的訓練資料，先依照其低階特徵分成若干個群組，再透過特徵擷取子系統分別擷取顏色、形狀、材質等低階特徵，利用兩兩比對的方式可以得到此三種低階特徵的相似度，可以求得對於此物件圖像而言，最佳的一個低階特徵組合，也就是建立此物件種類的比對模組。經過這些模組的建立之後，當使用者輸入一圖像來做檢索時，就可以與這些已建立好的模組作比對，將符合的模組名稱以及模組裡面的圖像回傳給使用者。透過這樣的機制，不僅可以增加圖像檢索的準確率，也可以自動的對圖像加註或是做圖像的分類。

With the development of the internet, the image retrieval systems are very widespread. So the most important thing is to design an image retrieval system with high efficiency and high precision. If we want to design a content-based image retrieval system, there could be a problem that which low level feature is better for comparison. It is suitable for some kinds of object, but the others are not.

  The problem could be solved with our system. We adjust the proportion of the low level features by utilizing the similarity of numerous training data of the object. It makes the recognition the object reach highest and there would be a combination of the low level features which is the most suitable with different kinds of object. After the result of the experiment, we establish the associations between image semantics and low level features. 

  To test the actually system, we utilize the image database to get the image and choose several kinds of object that the module we want to establish. By utilizing a large number of training data, first we classify them into several groups with their low level features, including color, shape and texture. Then we extract the low level features in the feature extraction subsystem. By comparing to each other, we can get these three similarities of the low level features. And then we can find the most suitable combination of the low level features of the object out. That is to say, we establish the comparing modules of the object. After establishing these comparing modules, a user give a query by inputting an image, it will be compared by all the modules we establish. Finally, the name and the images of module will be returned to the user. Through this mechanism, not only the precision of the image retrieval system will be increased but also we can realize image automatic annotation or classification.

第1章	緒論......................................... 4
1.1	研究動機與目的................................. 4
1.2	研究內容........................................ 8
1.3	論文內容大綱...................................10

第2章	背景知識與相關研究.....................11
2.1 圖像模糊化...................................... 11
2.1.1	平滑法..................................... 11
2.1.2	中值法..................................... 12
2.1.3	高斯法..................................... 14
2.2 圖像分割........................................ 15
2.2.1	區域成長法................................. 16
2.2.2	K平均演算法................................18
2.2.3	期望值最大化............................... 19
2.3 以內容為基礎的圖像檢索系統....................22
2.3.1	IBM的QBIC..................................22
2.3.2	Intelligent Shape Selection.................24
2.3.3	Semantics-sensitive Integrated Matching for Picture Libraries ..........................28

第3章	系統架構與圖像檢索系統............... 30
3.1	系統架構..................................... 30
3.2	圖像資料庫與圖像前處理.................... .33
3.2.1	圖像資料庫................................ 33
3.2.2	圖像前處理................................ 34
3.3	特徵擷取子系統.............................. 39
3.3.1	顏色擷取.................................. 40
3.3.2	形狀擷取.................................. 43
3.3.3	材質擷取.................................. 51
3.4	索引子系統與特徵資料庫..................... 54
3.4.1	索引子系統................................ 54
3.4.2	特徵資料庫................................ 55
3.5	圖像檢索子系統.............................. 57

第4章	系統實作與討論.......................... 59
4.1	實作環境介紹................................ 59
4.2	實作說明..................................... 60
4.3	系統評估與討論.............................. 67

第5章	結論與未來研究方向.....................74
5.1	結論..........................................74
5.2	未來研究方向.................................75

參考文獻............................................. 78

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