Robust video fingerprinting scheme based on contourlet hidden Markov tree model

Abstract

Video fingerprints are feature vectors that uniquely characterize a video clip and can be used for video indexing and copyright applications. This paper presents a novel video fingerprinting method based on the Contourlet hidden Markov tree model and Singular Value Decomposition. The Contourlet transform is a two-dimensional extension of the wavelet transform using multiscale and directional filter banks. It effectively captures the smooth contours that are the dominant features in natural images. The Contourlet HMT model can capture all inter-scale, inter-direction, and inter-location dependencies of the contourlet coefficients using a few statistics parameters. These parameters are robust against common content- preserving operations such as lossy compression, additive noise, filtering etc. We introduce SVD to compress the parameter matrix for generating the video fingerprint. The performance of the proposed fingerprint is experimentally evaluated and compared to the Centroid of Gradient Orientations based method. The proposed method is shown to have a better overall performance in robustness and video identification.