Video Copy Detection Using Temporally Informative Representative Images

Content-based video hashing was introduced recently to serve the purpose of video copy detection. A conventional approach to video hashing is to apply image hashing techniques to either every frame or to the selected key frames of a video sequence. Both approaches ignore the temporal information contained in a video sequence. This study proposes an approach for generating representative images of a video sequence that carry the temporal as well as the spatial information. These images are denoted as TIRIs, Temporally Informative Representative Images. Performance of the proposed approach is demonstrated by applying a simple image hashing technique on TIRIs of a video database. It is shown that the resulted video hashing algorithm is highly robust to noise, frame dropping, changes in brightness and contrast, as well as a range of geometric attacks. An average true positive rate of 99.2% and false positive rate of 0.4% of the proposed approach demonstrate the robustness and uniqueness of the generated hashes. It is demonstrated that the proposed approach is easy to implement and computationally more efficient than another state-of-the-art video hashing method.