Summarization scheme based on near-duplicate analysis

International audienceThis paper presents our approach to select relevant sequences from raw videos in order to generate summaries to Trecvid 2008 BBC Rush Task. Our system is composed of two major steps: First, the system detects \semantic" shot boundaries and keeps only non-redundant shots; then, the system esti- mates average motion for each shot, as a criterion of amount of information, to better share out the duration of the sum- mary between remaining shots. The rst step is based on a fast near-duplicate retrieval using Locality Sensitive Hashing (LSH) which provides results in few seconds (if we do not take into account decoding and encoding processes). The evaluation of Trecvid shows very promising results, since we ranked 17th over 43 runs, regarding redundancy measure (RE), and 18th for object and event inclusion (IN). These balanced results (most of best teams for the rst criterion are among the latest for the second one) show that our method o ers a quite good trade-o between false negatives (IN) and false positives (RE)

Generating comprehensible summaries of rushes sequences based on robust feature matching

This paper describes our first attempt at tackling a pilot task in Trecvid: video summarization of rushes data [3]. Our method is based on the tight clustering produced via SIFT matching. In this first attempt, we try to examine how our approach performs without complex implementation in terms of concept detection and excerpt assembly (i.e, no picture-in-picture, split screen and special transitions). Although we do not perform very well in terms of concept inclusion, we rank very well in terms of the summary being easy to understand and relevancy of included segments.<br /