CBCD Based on Color Features and Landmark MDS-Assisted Distance Estimation

Content-Based Copy Detection (CBCD) of digital videos is an important research field that aims at the identification of modified copies of an original clip, e.g., on the Internet. In this application, the video content is uniquely identified by the content itself, by extracting some compact features that are robust to a certain set of video transformations. Given the huge amount of data present in online video databases, the computational complexity of the feature extraction and comparison is a very important issue. In this paper, a landmark based multi-dimensional scaling technique is proposed to speed up the detection procedure which is based on exhaustive search and the MPEG-7 Dominant Color Descriptor. The method is evaluated under the MPEG Video Signature Core Experiment conditions, and simulation results show impressive time savings at the cost of a slightly reduced detection performance

Video Copy Detection Utilizing Log-Polar Transformation

Video Copy Detection is the process of comparing two videos to determine their similarity and determine if they are copies. This thesis enhances some of the common algorithms used in Video Copy Detection by utilizing the Log-Polar transformation as a pre-processing step. This pre-processing step is expected to increase speed of the overall Video Copy Detection process while maintaining the accuracy of the algorithms. The results of this research show that the addition of a Log-Polar pre-processing step reduces the computation time of the overall Video Copy Detection process. The additional time necessary to perform the Log-Polar pre-processing step is outweighed by the overall reduction in computation time. The accuracy and recall are slightly affected by the addition of this pre-processing step. The results also show that the video frame size can be significantly compressed with minimal effect to the algorithm\u27s overall performance