Efficient and Robust Detection of Duplicate Videos in a Large Database

Anindya Sarkar(1), Vishwakarma Singh(2), Pratim Ghosh(1), B. S. Manjunath(1), Ambuj Singh(2)
(1) Department of Electrical and Computer Engineering, University of California, Santa Barbara
(2) Department of Computer Science, University of California, Santa Barbara
(1) {anindya, pratim, manj} [at] ece.ucsb.edu, (2) {vsingh, ambuj} [at] cs.ucsb.edu


We present an efficient and accurate method for duplicate video detection in a large database using video fingerprints. We have empirically chosen the Color Layout Descriptor, a compact and robust frame based descriptor, to create fingerprints which are further encoded by vector quantization. We propose a new non-metric distance measure to find the similarity between the query and a database video fingerprint and experimentally show its superior performance over other distance measures for accurate duplicate detection. Efficient search can not be performed for high dimensional data using a non-metric distance measure with existing indexing techniques. Therefore, we develop novel search algorithms based on precomputed distances and new dataset pruning techniques yielding practical retrieval times. We perform experiments with a database of 38000 videos, worth 1600 hours of content. For individual queries with an average duration of 60 sec (about 50% of the average database video length), the duplicate video is retrieved in 0.032 sec, on Intel Xeon with CPU 2.33GHz, with a very high accuracy of 97.5%.
[PDF] [BibTex]
Anindya Sarkar, Vishwakarma Singh, Pratim Ghosh, B. S. Manjunath, Ambuj Singh,
IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no. 6, pp. 870-885, Jun. 2010.
Node ID: 536 , DB ID: 343 , Lab: VRL , Target: Journal
Subject: [Managing Multimedia Databases] « Look up more