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研究生:何宗達
研究生(外文):Tsung-Ta Ho
論文名稱:針對局部性特徵點可縮放式的索引方法
論文名稱(外文):A Scalable Indexing Method for Local Invariant Features
指導教授:吳怡樂
指導教授(外文):Yi-Leh Wu
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:資訊工程系
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:2008
畢業學年度:96
語文別:英文
論文頁數:24
中文關鍵詞:尺寸不變性特徵點主成份分析的尺寸不變性特徵點索引數位版權管理系統
外文關鍵詞:Sale invariant feature transform (SIFT)PCA-SIFTIndexingDigital management system
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在影像辨識的領域及數位版權管理中的浮水印,局部不變性特徵點已是被廣泛使用的方法,而尺寸不變性特徵轉換法是最常被使用來截取特徵點,此方法所取出的特徵點用於不同影像之間的辨識具有相當的可靠性,並且,這些特徵點能抵抗影像轉檔、尺寸縮放、旋轉等變化;然而,大量的特徵點對辨識階段將是一個重要的問題,影像的匹配會因為影像的數量而增加。
我們提出一個方法,利用二元化精簡的表示尺寸不變性特徵。我們也發現二元化的尺寸不變性特徵可以維持很高的匹配準確率;此外,可以用精簡易於擴充的雜湊方法有效率的建立二元化尺寸不變性特徵的索引(例如dbm及其他dbm形態)。我們在較大的資料庫裡(大約1400萬個特徵點),實作一個尺寸不變性特徵的匹配引擎來證實我們的假設,結果指出我們提出的方法在影像批配的應用中,可以有效率的減輕因為資料量的擴充所產生的問題。
The local invariant features have been widely used in the image recognition and in the Digital Right Management (DRM) applications. Among all the local invariant features, the Scale Invariant Feature Transform (SIFT) is one of the most adopted method. The feature keypoints extracted by the SIFT are invariant to image translation, scaling, rotation, etc., and hence a promising technique to identify plagiarism among images. However, the large number of local invariant feature keypoints extract by the SITF poses a scalability problem in the matching stage as the number of images to be matched increases.
We propose a method to simplified the SIFT descriptor through binarization. We discover that the binarized SIFT descriptors still maintain very high distinctiveness for the image matching process. Moreover, the binarized SIFT descriptors can be indexed by simple extensible hashing method (e.g., Dbm and its variations) very efficiently. We verify our hypothesis by implementing a SIFT descriptors matching engine with a large scale of data (> 14M keypoints). The results suggest that the proposed method can effectively alleviate the scalability problem in image matching applications.
1. Introduction ..........................................................- 1 -
2. SIFT ..................................................................- 3 -
3. PCA-SIFT ..........................................................- 5 -
4. Propose DRM System ..................................................- 6 -
4.1 Berkeley Database ..........................................- 6 -
4.2 JDBM ..........................................................- 6 -
4.3 Pre-process dataset ..........................................- 7 -
4.4 Feature Reduction ..........................................- 7 -
4.5 Indexing Structure ..........................................- 7 -
4.6 Retrieving Process ..........................................- 7 -
5. Experiment (Berkeley DB) ..........................................- 14 -
5.1 BDB with non-duplicate data ..................................- 14 -
5.2 BDB with duplicate data ..........................................- 15 -
5.3 Scalability with different sizes of training dataset (BDB) ..- 16 -
5.4 Query accuracy for geometric distorted images (BDB) ..........- 18 -
6. Experiment (JDBM) ..................................................- 19 -
6.2 JDBM with non- duplicate data ..................................- 19 -
6.2 JDBM with duplicate data ..................................- 19 -
6.3 Scalability with different sizes of training dataset (JDBM) ..- 20 -
6.4 Query accuracy for geometric distorted images (JDBM) ..........- 21 -
7. Conclusions ..........................................................- 22 -
References ..........................................................- 23 -
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[3]Y. Ke and R. Sukthankar. PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages. 511-517, 2004.
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[6] Ritendra Datta , Jia Li , James Z. Wang. Content-based image retrieval: approaches and trends of the new age. Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, pages. 253-262, 2005.
[7] A. E. Abdel-Hakim and A. A. Farag. CSIFT: A SIFT descriptor with color invariant characteristics. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages. 1978–1983, 2006.
[8] J.M. Geusebroek, R. van den Boomgaard, A.W.M. Smeulders, and H. Geerts. Color Invariance. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 12, pages. 1338-1350, 2001.
[9] Berkeley database, http://www.oracle.com/technology/products/berkeley-db/index.html
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[15] Alex Andoni’s LSH page, http://web.mit.edu/andoni/www/LSH/index.html
[16] Yan Ke , Rahul Sukthankar. Larry Huston, An efficient parts-based near-duplicate and sub-image retrieval system. Proceedings of the 12th annual ACM international conference on Multimedia, pages. 869-876 , 2004.
[17] A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. Proceedings of International Conference on Very Large Databases, pages. 518-529, 1999.
[18] JDBM, http://jdbm.sourceforge.net/
[19] H. Y. Lee, H. Kim, and H.K. Lee. Robust image watermarking using local invariant features. Optical Engineering, v.45 n.3, 037002, 2006.
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