K. Kaabneh and H. Al-Bdour

With the explosion of the Internet and digital imaging, processing and accessing videos in video databases become very important. For easy access and retrieval of stored videos, there is a clear need for indexing. A substantial part of video indexing is video segmentation, which is the process of dividing a video clip into coherent segments of similar characteristics. By using a reference image for each segment, those reference images can then be indexed to reference the different segments of that video clip.

This paper introduces a new segmentation technique that takes motion into consideration. The technique works by dividing each frame into blocks of a certain size; starting from a reference frame, each block is compared with all the blocks in a region surrounding the corresponding block in the next frame. To measure the similarity between frames, a technique combining mean square error and hit-ratio is used; the mean square error is used to measure the similarity between blocks, and the percentage (hit-ratio) of blocks passing that similarity test must exceed a preset threshold. Different values of block sizes, motion displacement, and hit-ratios are considered in this paper to optimize segmentation. A block size of 16, a displacement of 6 and a hit-ratio of 70% are found to be the best. Our segmentation technique has advantages over others such as motion sensitivity, adjustable segmentation granularity, and close agreement with subjective notion of visual segments.