This paper proposes a robust video fingerprinting method based on 2-dimensional oriented principal component analysis (2D-OPCA) of affine covariant regions. The goal of video fingerprinting is to identify a video clip using perceptual features called fingerprints. In the proposed method, to achieve the robustness against geometric transformations, fingerprints are extracted from local regions co- variant with a class of affine transformations. The detected affine covariant regions are normalized geometrically and photometrically, and local fingerprints are extracted by applying a novel discriminant analysis algorithm, 2D-OPCA to the normalized regions. For the reliable matching of local fingerprints, only spatio-temporally consistent matches are taken into account. The experimental results show that the proposed method is robust against both geometric and non-geometric transformations.
['Sunil Lee', 'Chang Dong Yoo']
Robust video fingerprinting based on 2D-OPCA of affine covariant regions