Efficient and Robust Detection of Duplicate Videos in a Database

In this paper, the duplicate detection method is to retrieve the best matching model video for a given query video using fingerprint. We have used the Color Layout Descriptor method and Opponent Color Space to extract feature from frame and perform k-means based clustering to generate fingerprints which are further encoded by Vector Quantization. The model-to-query video distance is computed using a new distance measure to find the similarity. To perform efficient search coarse-to-fine matching scheme is used to retrieve best match. We perform experiments on query videos and real time video with an average duration of 60 sec; the duplicate video is detected with high similarity

Video copy detection using multiple visual cues and MPEG-7 descriptors

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

Statistical similarity search applied to content-based video copy detection

Abstract—Content-based copy detection (CBCD) is one of the emerging multimedia applications for which there is a need of a concerted effort from the database community and the computer vision community. Recent methods based on interest points and local fingerprints have been proposed to perform robust CBCD of images and video. They include two steps: the search of similar fingerprints in the database and a voting strategy that merges all the local results in order to make a global decision. In most image or video retrieval systems, the search of similar features in the database is performed by a geometrical query in a multidimensional index structure. Recently, the paradigm of approximate knearest neighbors query has shown that trading quality for time can be widely profitable in that context. In this paper, we propose a new approximate search paradigm dedicated to local fingerprints and we describe the original indexing structure we have developped to compute efficiently the corresponding queries. We consider that the distribution of the relevant fingerprints around a query can be modeled by the distribution of the distorsion vector between a referenced fingerprint and a candidate one. Experimental results show that these statistical queries allow high performance gains compared to classical ǫ-range queries. By studying the influence of this approximate search on a complete CBCD scheme based on local video fingerprints, we also show that trading quality for time during the search does not degrade seriously the global robustness of the system, even with very large databases including more than 10,000 hours of video. I