Content-based video indexing and retrieval using the Radon transform and pattern matching

Content-based video indexing and retrieval exhibits many challenging problems. Methods developed for processing of video for content search can be roughly categorized into temporal segmentation, spatial segmentation, and spatio-temporal video segmentation. The temporal segmentation aims to divide video into clips or (usually) camera shots. The spatial segmentation, on the other hand, seeks for the ROI’s (regions of interests) in video frames. The spatio-temporal segmentation is, however, more general since the video data is essentially spatial and temporal domain function in nature. It is expected that spatiotemporal methods describe the video content more completely and accurately as compared to the temporal and spatial segmentation of video independently. In the presented work, we approach the video-indexing problem by means of the spatio-temporal Radon projections. Specific projections are chosen for indexing the extracted features of a video clip. Pattern matching and pattern search are also studied via these projection-based features. Experiments show promising results for our approach.