Net Comparison: A Fast and Effective Method for Classifying Image Sequences

As video information proliferates, managing video sources becomes increasingly important. Automatic video partitioning is a prerequisite for organizing and indexing video sources. Several methods have been introduced to tackle this problem, e.g. pairwise and histogram comparisons. Each has advantages, but all are slow because they entail inspection of entire images. Furthermore none of these methods have been able to deene camera break and gradual transition, which are basic concepts for partitioning. In this paper, we attempt to deene camera break. Then, based on our deenition and probability analysis, we propose a new video partitioning algorithm, called NET Comparison (NC), which compares the pixels along predeened net lines. In this way, only part of the image is inspected during classiication. We compare the eeec-tiveness of our method with other algorithms such as pairwise, likelihood and histogram comparisons, evaluating them on the basis of a large set of varied image sequences that include camera movements, zooming, moving objects, deformed objects and video with degraded image quality. Both gray-level and HSV images were tested and our method out-performed existing approaches in speed and accuracy. On average, our method processes images two to three time faster than the best existing approach.