Machine Learning for Video Processing

Video Fingerprinting



Robust Video Fingerprinting for Content-Based Video Identification


Sunil Lee and Chang D. Yoo


Video fingerprints are feature vectors that uniquely characterize one video clip from another. The goal of video fingerprinting is to identify a given video query in a database (DB) by measuring the distance between the query fingerprint and the fingerprints in the DB. The performance of a video fingerprinting system, which is usually measured in terms of pairwise independence and robustness, is directly related to the fingerprint that the system uses. In this paper, a novel video fingerprinting method based on the centroid of gradient orientations is proposed. The centroid of gradient orientations is chosen due to its pairwise independence and robustness against common video processing steps that include lossy compression, resizing, frame rate change, etc. A threshold used to reliably determine a fingerprint match is theoretically derived by modeling the proposed fingerprint as a stationary ergodic process, and the validity of the model is experimentally verified. The performance of the proposed fingerprint is experimentally evaluated and compared with that of other widely-used features. The experimental results show that the proposed fingerprint outperforms the considered features in the context of video fingerprinting.

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Related Papers

1. Sunil Lee and Chang D. Yoo, "Robust Video Fingerprinting for Content-Based Video Identification," IEEE Trans. Circuits and Systems for Video Technology, vol. 18, no. 7, pp. 983-988, July 2008.

2. Sunil Lee and Chang D. Yoo, "Video Fingerprinting Based on Centroids of Gradient Orientations," In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse, France, vol. 2, pp. 401-404, May 2006.