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研究生:鍾岳儒
研究生(外文):Yueh-Ju Chung
論文名稱:基於Cloud平台的近似商標圖像系統
論文名稱(外文):A Cloud-Based Resembling Trademark Images Retrieval System
指導教授:呂紹偉
指導教授(外文):Shao-Wei Leu
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:電機工程學系
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:2012
畢業學年度:100
語文別:中文
論文頁數:65
中文關鍵詞:內容式圖像檢索尺度不變特徵轉換特徵袋碼位置敏感雜湊
外文關鍵詞:CBIRSIFTBoFLSHDotCloud
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  • 下載下載:30
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為確保商標所有者之權益,商標在使用之前必須先行註冊,並且應避免採用與其他已註冊之商標近似之商標設計,以免造成侵權。因此,如何迅速、有效地搜尋商標資料庫中潛在的近似商標,是一個重要的課題。
為解決前述近似商標檢索問題,本論文使用尺度不變特徵轉換(SIFT)演算法擷取商標圖像之區域特徵,然後以BoF演算法縮減商標圖像特徵維度,再使用位置敏感雜湊(LSH)演算法進行近似商標圖像檢索。經由上述各演算法的處理,使用者可以進行商標搜尋比對,找出近似商標,以可以將商標圖像加入資料庫。由於商標數量不斷增加,商標搜尋比對的計算量也將日益龐大,為了維持系統運作的效率以及提供系統擴充的高度彈性,我們以IaaS模式將系統佈署在DotCloud平台上。資料庫是以MySQL實現,網頁開發框架則使用Django。

Registering a trademark is necessary for the protection of the trademark owner’s legal rights. Conversely, when registering a trademark, one must be very careful not to violate the rights of exiting trademarks owners. Therefore, it is highly desirable to be able to search and retrieve from the database all trademarks that may appear similar to the one about to be registered.
Attempting to fulfill the goal stated above, this research first uses the Scale Invariant Feature Transformation (SIFT) algorithm to extract local features of trademark images, then the Bag of Features (BoF) model to obtain vocabulary-based representation of the images. Finally, the Locality Sensitive Hashing (LSH) is applied to facilitate efficient indexing and retrieval of the images. The users of our system can search the database to retrieve resembling trademarks for further examination, and they can also add new trademark images into the database if so desire.
Because the amount of trademark images will only continue to grow, for future scalability of the system and to gain experience on cloud computing, we choose to deploy our database on Dot-Cloud, an IaaS-style cloud system. The database was implemented with MySQL and the Web program was developed with Django.

第 一 章 緒論 1
1.1 研究動機 1
1.2 問題現況 1
1.3 研究目的 5
1.4 論文內容提要 6
第 二 章 文獻探討 7
2.1 Cloud Computing 7
2.1.1 Cloud Computing的基本特徵 7
2.1.2 Cloud Computing四個建置模式 8
2.1.3 Cloud Computing三個服務模式 9
2.2 內容式圖像檢索 10
2.3 局部特徵 11
2.4 最鄰近搜尋(Nearest Neighbor Search, NNS) 12
2.4.1 KD-Tree演算法 13
2.4.2 LSH (Locality Sensitive Hashing)演算法 15
第 三 章 相關演算法介紹 16
3.1 商標圖像處理流程 16
3.2 SIFT演算法 16
3.2.1 構建尺度空間以獲得尺度不變性 17
3.2.2 特徵點精確定位 19
3.2.3 特徵點分配方向值 20
3.2.4 特徵點描述 21
3.3 BoF碼 22
3.4 LSH演算法 24
3.4.1 嵌入(embedding) 25
3.4.2 投影(projection) 26
3.4.3 計算Hamming distance 27
第 四 章 系統架構與操作方法 28
4.1 系統架構 28
4.2 系統操作 30
第 五 章 實驗與結果分析 36
5.1 實驗商標來源 36
5.2 實驗檢定 37
5.3 實驗結果檢討與分析 45
第 六 章 結論與未來發展 48
6.1 結論 48
6.2 未來發展 48

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