Semantic Video Annotation using Background Knowledge and Similarity-based Video Retrieval
We describe our experiments for the High-level Feature Extraction (FE) and Search (SE) tasks. We submitted two automatic runs to the FE task, the rst one (MMIS alexei) was based on a probabilistic approach while the second (MMIS ainhoa) was an enhanced version that used background knowledge in the form of statistical co-occurrence of annotation keywords. While previous applications of this approach to other datasets have performed quite well, our results in TRECVID 2008 are not so good. In particular, the performance of the second run was limited by the small vocabulary. For the SE task we submitted two runs: a similarity-based media search (MMIS media) and the required text-only search (MMIS text). The similarity search, using media content, had better precision than the text-only search but had diculties with some types of queries (e.g., motion-based). Overall, participation in the TRECVID evaluation was a valuable learning experience for our group.
similarity search,feature extraction
Annotation,Video retrieval,Information retrieval,TRECVID,Computer science,Image retrieval,Feature extraction,Probabilistic logic,Vocabulary,Nearest neighbor search