On improving robustness of video fingerprints based on projections of features

In this paper, we study two methods to improve the robustness property of projection based hashing methods. For this class of hashing methods, a feature matrix is projected onto a set of projection matrices. Then, the projected values are compared to a threshold to derive the hash bits. In our previous work [4], we showed that the collision characteristics of these methods can be optimized by a careful selection of the projection matrices. The projection matrices were obtained using a Singular Value Decomposition (SVD) on a set of features from a training data set that minimized the cross-correlation between projected values. However, these projection matrices did not consider the effect of content modifications on the projected values