In Near-Duplicate Video Retrieval (NDVR), recent works have focused on bettering index structures and matching schemes not only to improve retrieval accuracy but also to enforce scalability in an effort to keep up with the ever-growing size of video collections. In this paper, we propose a framework for the retrieval of Near-Duplicate Videos (NDVs) based on a pre-processing step of clustering inspired by Multiple Sequence Alignment (MSA) of DNA sequences. In our technique, we represent videos as alphabetical genomes and MSA is employed to automatically cluster a video collection. NDVR is then conducted on these formed clusters instead of the original video collection. Experimentally, we show that our clustering-based approach, while being significantly faster than state-of-the-art techniques that are not based on a pre-processing clustering step, i.e. the n-gram and Dynamic Time Warping (DTW) techniques, yields equivalent results in a precision/recall evaluation framework.

Building accurate models for video event classification is an important research issue since they are essential components for effective video indexing and retrieval. Recently kernel-based methods, particularly support vector machines, have become popular in multimedia classification tasks. However, in order to use them effectively, several factors that hinder accurate classification results, such as feature subset selection and selection of the SVM kernel parameters, must be addressed through the use of heuristic-based techniques. We present a new approach to enhance the performance of SVM for video events classification based on a search method. The latter relies on the simultaneous optimization of the feature and instance subset and SVM kernel parameters, with genetic algorithms. Classification results on sport videos show the significant improvement over conventional SVM.

In this paper, we propose the use of motion tracks to generate video features, which are applied to perform video retrieval. We simply use the location of the object's center-of-gravity and its velocity across consecutive video frames to from motion flows, which are then recorded and stored in a video database. In the video retrieval phase, we propose the use of n-gram matching strategy to execute the video retrieval task. The retrieval process can be triggered query-by-example. We identify corresponding query and index contents accurately in order to detect "similar" videos. Experiments are carried out on CMU database. An empirical comparison over the state-of-the-art dynamic programming techniques is encouraging and demonstrates the advantage and feasibility of the n-gram method.

In this paper, we describe methods for identification of rebroadcasted video sequences. The methods are applicable to real-time monitoring of video streams, or to searching of large collections. The techniques we present are fast and insensitive to video degradation, making them ideal for re-broadcasted video search. Experiments on large quantities of real-world video data demonstrate that our approach is robust and effective, even for short queries or queries that have been edited or degraded. By using dynamic programming and N-gram matching approaches we identify regions of similarity in video signatures efficiently and accurately in order to identify rebroadcasted videos, even when these regions constitute only a small section of the clip being searched.