Browsing by Subject "Video copy detection"

Now showing 1 - 3 of 3

Open Access
Content-based video copy detection based on motion vectors estimated using a lower frame rate
(Springer U K, 2014-09) Taşdemir K.; Çetin, A. Enis
We propose a motion vector-based video content-based copy detection method. One of the signatures of a given video is motion vectors extracted from image sequences. However, when consecutive image frames are used, the resulting motion vectors are not descriptive enough because most vectors are either too small or they appear to scatter in all directions. We calculate motion vectors in a lower frame rate than the actual frame rate of the video to overcome this problem. As a result, we obtain large vectors and they represent a given video in a robust manner. We carry out experiments for various parameters and present the results. © 2014 Springer-Verlag London.
Open Access
Motion vector based features for content based video copy detection
(IEEE, 2010) Taşdemir, K.; Çetin, A. Enis
In this article, we propose a motion vector based feature set for Content Based Copy Detection (CBCD) of video clips. Motion vectors of image frames are one of the signatures of a given video. However, they are not descriptive enough when consecutive image frames are used because most vectors are too small. To overcome this problem we calculate motion vectors in a lower frame rate than the actual frame rate of the video. As a result we obtain longer vectors which form a robust parameter set representing a given video. Experimental results are presented. © 2010 IEEE.
Open Access
Video copy detection using multiple visual cues and MPEG-7 descriptors
(Academic Press, 2010) Küçüktunç, O.; Baştan M.; Güdükbay, Uğur; Ulusoy, Özgür
We propose a video copy detection framework that detects copy segments by fusing the results of three different techniques: facial shot matching, activity subsequence matching, and non-facial shot matching using low-level features. In facial shot matching part, a high-level face detector identifies facial frames/shots in a video clip. Matching faces with extended body regions gives the flexibility to discriminate the same person (e.g., an anchor man or a political leader) in different events or scenes. In activity subsequence matching part, a spatio-temporal sequence matching technique is employed to match video clips/segments that are similar in terms of activity. Lastly, the non-facial shots are matched using low-level MPEG-7 descriptors and dynamic-weighted feature similarity calculation. The proposed framework is tested on the query and reference dataset of CBCD task of TRECVID 2008. Our results are compared with the results of top-8 most successful techniques submitted to this task. Promising results are obtained in terms of both effectiveness and efficiency. © 2010 Elsevier Inc. All rights reserved.