Experimenting VIREO-374: Bag-of-Visual-Words and Visual-Based Ontology for Semantic Video Indexing and search

In this paper, we present our approaches and results of high-level feature extraction and automatic video search in TRECVID-2007. In high-level feature extraction, our main focus is to explore the upper limit of bag-of-visualwords (BoW) approach based upon local appearance features. We study and evaluate several factors which could impact the performance of BoW. By considering these important factors, we show that a local feature only system already yields top performance (MAP= 0.0935). This conclusion is similar to our recent experiment of VIREO-374 on TRECVID-2006 dataset [1], except that the improvement, when incorporating with other features, is marginal. Description of our submitted runs: