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研究生(外文):Ya-Fen Cheng
論文名稱(外文):Figurative Trademark Retrieval System based on Local Descriptors
指導教授(外文):Jen-Chang Liu
外文關鍵詞:TrademarkLocal DescriptorsImage Search
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本論文主要研究為圖像商標檢索。在當今全球化經濟中智慧財產權 (Intellectual Property Right, IPR) 已日益重要,為了保護商標所有人的權益,搜尋 (Search) 和監看 (Watch) 是否有仿冒或盜用圖像商標的情況具有重要的應用價值。我們利用三種影像局部特徵子作為特徵擷取與比對的方式,分別為SIFT、PCA-SIFT和SURF特徵,主要目的要找出對於圖像商標有最好效能的特徵子。商標資料庫中有著相當大量的影像,而影像中所包含的局部特徵子亦非常多,因此利用窮舉法搜尋相當費時,為了加快搜尋的速度,我們利用了字彙樹 (Vocabulary tree) 作為影像搜尋的方法,但是當速度變快時,相對的準確率也會降低,因此要在準確率與速度之間取得一個平衡。本論文分別利用改良的貪婪的N條最佳路徑搜索 (Greedy N-Best Paths Search) 和幾何校正 (Geometric Rectification) 提升準確率。在200張查詢影像以及台灣經濟部智慧財產局27,610張資料庫影像的實驗中,準確率可高達九成左右。
Attributing to the development of informational diversification, users can directly acquire a large number of information which is useful and surfed immediately from the browser. Nowadays, the technique of text retrieval has been developed more maturely, such as “Google search”. However, text-only search is not enough for the variety of resource from the internet, because there are more and more types of information in the world, such as video, audio and the combinations of them.
Nowadays, intellectual property right in the globalization economy has received much attention, and watch for infringed trademarks is one of the most important issues. This thesis focuses on figurative trademark search on the image database collected at the Ministry of Economic Affairs Intellectual Property Bureau, Taiwan. To search for similar trademarks using image content, we make use of three kinds of features including SIFT, PCA-SIFT and SURF for matching between the images. The first goal is to find out which feature is most suitable for the trademark database. Because of the large amount of features extracted from the images, the off-line training and on-line processing both spend a lot of time to finish. To improve the performance, we use the vocabulary tree for reducing the search time. While the search efficiency is improved, the search accuracy becomes slight poor. For this purpose, two modifications of the greedy N-best paths search and geometric rectification are used for reclaiming the accuracy. In the experiments, 200 images are used as queries for searching 27,610 images in database, and the accuracy of the performance can be up to 90% or so.
誌謝 I
論文摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章、導論 1
1.1. 研究動機與目的 1
1.2. 文獻探討 5
1.2.1經濟部智慧財產局的商標檢索 5
1.2.2基於內容式的影像檢索 11
1.2.3特徵點描述 14
1.3. 系統簡介 15
1.3.1資料庫建立流程 15
1.3.2系統流程 16
1.3.3系統概述 17
1.4. 論文大綱 18
第二章、影像特徵擷取 19
2.1 SIFT特徵 19
2.2 PCA-SIFT特徵 24
2.3 SURF特徵 25
第三章、影像搜尋方法與重新排序 29
3.1 窮舉法 29
3.2 字彙樹 29
3.3 貪婪的N條最佳路徑搜索 31
3.4 利用幾何校正重新排序 37
第四章、建立資料索引方法 40
4.1 資料庫建立 40
4.2 反向索引 41
4.3 定義權重 43
第五章、影像相似度計算 44
5.1 相似度方法介紹 44
5.2 Count Match 44
5.3 Binary feature vector以及L1、L2距離 45
第六章、實驗與討論 47
6.1 實驗環境 47
6.2 評估方法 48
6.3 實驗結果 51
6.3.1實驗一:窮舉法 51
6.3.2實驗二:使用字彙樹減少搜尋空間 52
6.3.3實驗三:在字彙樹上使用貪婪的N條最佳路徑搜索 54
6.3.4實驗四:在字彙樹上使用幾何校正 57
第七章、總結與未來展望 61
7.1 總結 61
7.2 未來展望 61
參考文獻 63
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