Visual Duplicates in Video using Content based Analysis

Neha Bhalerao, Kavita Pandey, Prachi Shinde, Ashwini Magar

Abstract


This paper proposes a technique to detect visual duplicates in video using content-based analysis for analyzing duplicate videos in the database. Content-based analysis refers to analysis of digital media on the basis of features extracted from its contents. The proposed algorithm uses key frames, extracts spatial features and stores them in their respective feature vector. Feature vector stores the features extracted from key frame in numerical form. It then compares feature vectors from the database to that of the query video and displays whether the query video is duplicated or not. The authors have analyzed only a set of key frames instead of an entire video frame. Thus, it is memory-efficient to detect duplicated video when we handle a vast size of the videos.

Keywords: visual duplicates, video, image, algorithm


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References


Hampapur A, Bolle R. Comparison of distance measures for video copy detection. Proc. of the IEEE Int. conf. on Multimedia and Expo (ICME). 2001; 737–40p.

Kim C, Vasudev B. Spatiotemporal sequence matching for efficient video copy detection. IEEE Transactions on Circuits and Systems for Video Technology. 2005; 15(1): 127–32p.

Roopalakshmi R, Ram Mohana Reddy G. Recent Trends in Content-Based Video Copy Detection. Information Technology Department, National Institute of Technology (NITK), Mangalore, Karnataka, India. 4. Tang Haiping, Ni Rongrong, Zhao Yao. Video Copy Detection Based on Median of Key Frames. Institute of Information

Science, Beijing Jiaotong University, Beijing, China.

Kim Joosub, Nam Jeho. Content-Based Video Copy Detection Using Spatio-Temporal Compact Feature. School of Mobile Communication & Digital Broadcasting Engineering, University of Science & Technology(UST), Daejeon, Korea. Broadcasting & Telecommunication Media Research Department, Electronics and Telecommunication Research Institute (ETRI), Daejeon, Korea.

Balakrishnan Shimna, Thakre Kalpana S. Video match analysis: A comprehensive content based video retrieval system. International Journal of Computer Science and Application. 2010. ISSN 0974-0767

Patel BV, Meshram Shah, Anchor BB. Kutchhi Polytechnic, Mumbai, India. Content based video retrieval. The International Journal of Multimedia & Its Applications (IJMA). October 2012; 4(5).

Cord M, Gosselin P-H, Philipp-Foliguet S. Stochastic exploration and active learning for image retrieval. Image and Vision Computing. 2007; 25: 14–23p.

Cámara-Chávez G, Precioso F, Cord M, et al. Shot boundary detection by a hierarchical supervised approach. 14th Int. Conf. on Systems, Signals and Image Processing (IWSSIP'07), Jun. 2007; 197–200p.

Snoek C, Worring M, Geusebroek J, et al. The semantic pathfinder: Using an authoring metaphor for generic multimedia indexing. IEEE Trans. Pattern Anal. Mach. Intell. Oct. 2006; 28(10): 1678–89p.

Catalan JA, Jin JS. Texture features for image retrieval. IEEE International Conference on Multimedia and Expo. 2000; 2: 1211–4p.

Marchand-Maillet Stéphane. CVBR page, useful links on content based video retrieval. URL: http://viper.unige.ch/video/index.html

Uchida Y, Agrawal M, Sakazawa S. Accurate content based video copy detection with efficient feature indexing. Proc. of ICMR. 2011.

Sivic J, Zissermane A. Video google: A text retrieval approach to object matching in videos. Proc. of ICCV. 2003; 1470–7p


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