In this paper, we propose a framework for detecting near duplicate copies of a video based on an ordinal method.
The framework also incorporates a bitmap indexing structure instead of a typical indexing structure used in the
previous published work. Using this method, two levels of indices are constructed. The first level of this process
groups each input video (represented by their key frames) into k clusters. These clusters and the associated key
frames are then used to construct the first level index. The second level of this process converts ordinal-based
video signatures (generated using the technique developed in earlier work) into bitmap vectors. By adopting
this two-level indexing scheme, query processing times are significantly reduced. This is because, the system
is required to match only videos in the clusters that are relevant to the query and not all the videos in the
database. Additionally, the technique implemented utilizes a bitmap structure for indexing, resulting in less
storage space. Furthermore, we are able to employ low-cost Boolean operations such as AND, OR, and XOR in
the matching process instead of Euclidean distance or other similar matching algorithms. This helps to reduce
the computational time for video matching. The system has demonstrated to effectively reduce the space needed
to store collections of video signatures in a database, as well as improving the overall system performance. In
addition, initial results show that the system is effective and robust to several transformations such as changes
in brightness, color, contrast, resolution (reduction) as well as the addition of noise.