應用於數位影片內容保護之空間域與時間域特徵抽取機制

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：6

、訪客IP：18.224.149.242

姓名

吳錦松(Chin-Song Wu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

應用於數位影片內容保護之空間域與時間域特徵抽取機制
(Spatial And Temporal Feature Extraction For Digital Video Copy Detection)

相關論文

★ 基於QT之跨平台無線心率分析系統實現	★ 網路電話之額外訊息傳輸機制
★ 針對與運動比賽精彩畫面相關串場效果之偵測	★ 植基於向量量化之視訊/影像內容驗證技術
★ 植基於串場效果偵測與內容分析之棒球比賽精華擷取系統	★ 以視覺特徵擷取為基礎之影像視訊內容認證技術
★ 使用動態背景補償以偵測與追蹤移動監控畫面之前景物	★ 應用於H.264/AVC視訊內容認證之適應式數位浮水印
★ 棒球比賽精華片段擷取分類系統	★ 利用H.264/AVC特徵之多攝影機即時追蹤系統
★ 利用隱式型態模式之高速公路前車偵測機制	★ 基於時間域與空間域特徵擷取之影片複製偵測機制
★ 結合數位浮水印與興趣區域位元率控制之車行視訊編碼	★ 應用於數位智權管理之H.264/AVC視訊加解密暨數位浮水印機制
★ 基於文字與主播偵測之新聞視訊分析系統	★ 植基於數位浮水印之H.264/AVC視訊內容驗證機制

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本研究提出一個應用於數位影片複製偵測之空間域與時間域特徵抽取機制，可應用於鑑別使用者上傳的影片之合法性。本研究方法是以影像內容的特徵為檢索基礎，並且提出新的空間域(spatial)特徵值為索引的快速近似方法來抵抗影片失真的狀況與提高辨識性，最後再使用新的時間域(temporal)特徵值的快速相似匹配方法，來鑑別使用者上傳的影片之合法性。本系統架構是基於H.264/AVC壓縮域的影片來執行解碼，進而分析每一組GOP(Group of Picture)值，來進行切換鏡頭畫面(SCF)的偵測分析，再從這些SCF集合中獲取時間域的數位特徵值，緊接著繼續從這些SCF集合中篩選出一個或是數個具代表此部Video的key frame，再透過我們提出的一種新的空間域特徵值提取方法來得到空間域的數位特徵值，最後我們僅需將產出的空間域與時間域的數位特徵值，儲存進資料庫中，並不需要花費大量的儲存空間來儲存原版影片。日後若需要鑑別使用者上傳的影片之合法性時，僅需比較在影片資料庫的空間域與時間域的數位特徵值即可。
我們提出的植基於內容的影片複製偵測方法，是適用於線上大量影片的複製偵測，例如鑑別使用者上傳到YouTube伺服器的影片之合法性。經過實際測試，在資料庫為252小時的影片中，使用者上傳影片的一張key frame的執行匹配計算時間約0.016秒。我們使用了MUSCLE-VCD-2007[34]與YouTube上大量影片來當作影片資料庫，並且使用一些失真的相似影片(例如在影片中加入noise、亮度改變、對比度改變、frame loss、frame insert、frame change、移位、旋轉、time shift)與不同影片來執行複製鑑別，實驗數據顯示了本機制是一個強健與高辨識性的系統，在對龐大的資料庫進行比較時，有高平均的查全率（Recall）與準確率（Precision），並能夠迅速地鑑別上傳的影片之合法性。

摘要(英)

In this research, the techniques of spatial and temporal feature extraction are proposed for digital video copy detection. An efficient content-based video copy detection scheme based on the spatial and temporal feature extraction and matching is presented. The key-frames are selected to generate the spatial features, which are used as the anchor points for the temporal feature matching. The design considers the video coding structure so the efficiency and the compact size of feature database are the main contributions of the proposed framework. The experimental results show that the extracted feature can facilitate fast content matching for identifying the possible copies. The method should thus be feasible in matching contents in very large video databases.

關鍵字(中)

★ 影片複製偵測
★ 基於內容之影片檢索

關鍵字(英)

★ video copy detection
★ CBVR

論文目次

摘要 i
Abstract ii
Contents iii
List of Figures iv
List of Table vi
1 Introduction 1
1.1 Motivation………………………………………………………………….….1
1.2 Challenges………………………….……………………………………….…3
1.3 Contribution…………………………………………………………………...5
1.4 Organization of Dissertation…………………………………………………..5
2 The Related Work 6
2.1 Feature extraction …………………………………………………………….7
2.1.1 Shot change detection techniques……………………………………….8
2.1.2 Key frame selection techniques……………….……………………….13
2.2 Summary……………………………………………………………………16
3 The Proposed Scheme 19
3.1 The shot-change detection………………………………………………….19
3.2 The key-frame selection……………………………………………………23
3.3 The spatial feature……………………………………………………………24
3.4 The temporal features………………………………………………………29
4 Experimental Results 35
5 Conclusion and Future Work 49
References List 50

參考文獻

[1] P. Geetha , Vasumathi Narayanan, “A Survey of Content-Based Video Retrieval,” Journal of Computer Science 4 (6), 2008, pp: 474-486.
[2] Bing Han, Xinbo Gao, Hongbing Ji, “A shot boundary detection method for news video based on rough-fuzzy sets,” International Journal of Information Technology, Vol. 11, No. 7, 2005, pp: 101-111.
[3] Gao, X. and X. Tang, “Unsupervised video-shot segmentation and model-free anchorperson detection for news video story parsing,” IEEE Trans. On Circuits and Systems for Video Technology, 2002, pp: 765-776.
[4] Gao, X. and X. Tang, “Automatic Parsing of News Video Based on Cluster Analysis,” Proceedings of Asia Pacific Conference on Multimedia Technology and Applications, Kaohsiung, Taiwai, China, Dec. 2000, pp: 17-19.
[5] Han Bing, Gao Xin-bo, Ji Hong-bing, “An efficient algorithm of gradual transition for shot boundary segmentation.” 3rd International Symposium on Multispectral Image Processing and Pattern recognition (MIPPR′03), Beijing, 2003, pp:956-961.
[6] Seung-Hoon Han, Kuk-Jin Yoon, and In So Kweon, “A new technique for shot detection and key frames selection in histogram space,” Workshop on Image Processing and Image Understanding, 2000, pp 475-479.
[7] H. Zhang, A. Kankanhalli, and S. W. Smoliar, “Automatic partitioning of full-motion video,” ACM Multimedia Systems, vol. 1,no.1, 1993, pp. 10–28.
[8] Bilge, G. and A.M. Tekalp, “Content based video abstraction,” Proceedings of the International Conference on Image, 1998, pp:128-132.
[9] John, S., Boreczky and D. Lynn, “A hidden markov model framework for video segmentation using audio an image features,” Proceedings of the IEEE International conference on Acoustics, Speech and Signal Processing, 1998, pp:3741-3744.
[10] Shan Li, Moon-Chuen Lee, “An improved sliding window method for shot change detection,” Proceeding of the 7th IASTED International Conference Signal and Image Processing, 2005, pp: 464-468.
[11] O′Toole, Colin and Smeaton, Alan F. and Murphy, Noel and Marlow, Sean, “Evaluation of automatic shot boundary detection on a large video test suite,” In: 2nd U.K. Conference on Image Retrieval: The Challenge of Image Retrieval, 1999, pp: 1-12.
[12] Cernekova, Z., N. Nikolaidis and I. Pitas, “Temporal video segmentation by graph partitioning,” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2006, pp:209-212.
[13] Hong Jiang Zhang, Jianhua Wu, Di Zhong, Stephen W. Smoliar,” An integrated system for content-based video retrieval and browsing,” Elsevier, Pattern Recognition, Volume 30, Issue 4, April 1997, Pages 643–658.
[14] Bilge Gunsel, A. Murat Tekalp, Peter J. L. van Beek ,“Content-based access to video objects: Temporal Segmentation, visual summarization, and feature extraction.,” Signal Processing 1998; 66(2):261-280.
[15] Wayne Wolf, “Key frame selection by motion analysis,” IEEE International Conference on Acoustics, Speech and Signal Processing, 1996, pp. 1228–1231,.
[16] K. Kashino, T. Kurozumi, and H. Murase, “A quick search method for audio and video signals based on histogram pruning,” IEEE Trans. On Multimedia, vol. 5, 2003, pp. 348–357.
[17] A. Ferman, M. Tekalp, and R. Mehrotra, “Robust color histogram descriptors for video segment retrieval and identification,” IEEE Trans. on Multimedia, vol. 11, 2002, pp. 497 – 508.
[18] M. Swain and D. Ballard, “Color indexing,” Int. J. Comput. Vis, vol. 7, 1991.
[19] W. Hsu, T. S. Chua, and H. K. Pung, “An integrated color-spatial approach to content-based image retrieval,” in Proc. ACM Multimedia, 1995.
[20] C. Cotsaces, N. Nikolaidis, and I. Pitas, “Video Shot detection and condensed representation - a review,” IEEE Signal Processing Magazine, vol. 23, 2006, pp. 28–37.
[21] Y. Zhuang, Y. Rui, T. S. Huang, and S. Mehrotra, “Adaptive key frame extracting using unsupervised clustering,” Proc. of IEEE Int Conf on Image Processing, 1998, pp. 866–870.
[22] T. Wang, Y. Wu, and L. Chen, “An approach to video key-frame extraction based on rough set,” Multimedia and Ubiquitous Engineering, 2007. MUE ’07. International Conference on, 2007, pp. 590–596.
[23] Y. P. Tan, D. D. Saur, S. R. Kulkarni, and P. J. Ramadge, “Rapid estimation of camera motion from compressed video with application to video annotation,” IEEE Transactions on Circuits and Systems for Video Technology, 2000, pp. 133–145.
[24] T. Liu, H.-J. Zhang, and F. Qi, “A novel video key-frame-extraction algorithm based on perceived motion energy mode,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, 2003, pp. 1006–1013.
[25] P.-H. Wu, T. Thaipanich, and C.-C. J. Kuo, “A suffix array approach to video copy detection in video sharing social networks,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, Taipei, Taiwan, Apr. 2009.
[26] C. D. Roover, C. D. Vleeschouwer, F. Lefebvre, and B. Macq, “Robust video hashing based on radial projections of key frames,” IEEE Transactions on Signal Processing, 2005.
[27] B. Coskun, B. Sankur, and N. Memon, “Spatio-temporal transform based video hashing,” IEEE Transactions on Multimedia, 2006, pp. 1190–1208.
[28] F. Zargari, M. Mehrabi, and M. Ghanbari, “Compressed domain texture based visual information retrieval method for I-frame coded pictures,” IEEE Transactions on Consumer Electronics, 2010, pp. 728–736.
[29] H. Ling, H. Cheng, Q. Ma, F. Zou, and W. Yan, “Efficient image copy detection using multiscale fingerprints,” IEEE MultiMedia, 2012, pp. 60–69.
[30] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. of Computer Vision, 2004, pp. 91–110.
[31] L.-W. Kang, C.-Y. Hsu, H.-W. Chen, and C.-S. Lu, “Secure sift-based sparse representation for image copy detection and recognition,” in IEEE International Conference on Multimedia Exposition, 2010, pp. 1248–1253.
[32] P.-C. Su, C.-C. Chen, and H.-M. Chang, “Towards effective content authentication for digital videos by employing feature extraction and quantization,” in IEEE Trans. on Circuits and Systems for Video Technology, to appear, vol. 19, May 2009, pp. 668–677.
[33] C. J. van Rijsbergen, Information Retrieval. London, UK: Butterworth- Heinemann, 1979.
[34] J. Law-To, A. Joly, and N. Boujemaa, “Muscle-VCD-2007: a live benchmark for video copy detection,” 2007, http://www-rocq.inria.fr/imedia/civr-bench/.
[35] ReefVid: A Resource of Free Coral Reef Video Clips for Educational Use [Online]. Available: http://www.reefvid.org.
[36] M. M. Esmaeili, M. Fatourechi, and R. K. Ward, “A robust and fast video copy detection system using content-based fingerprinting,” IEEE Transactions on Information Forensics and Security, 2011, pp. 213–226.
[37] Benchmark videos from Youtube [Online]. Available: http://www.video-comparer.com/product-benchmarkyoutube-list.php.

指導教授

蘇柏齊(Po-Chyi Su)

審核日期

2014-8-13

推文