Manifold-ranking based video concept detection on large database and feature pool

In this paper we discuss a typical case in video concept detection: to learn target concept using only a small number of positive samples. A novel manifold-ranking based scheme is proposed, which consists of three major components: feature pool construction, pre-filtering, and manifold-ranking. First, as there are large variations in the effective features for different concepts, a large feature pool is constructed, from which the most effective features can be selected automatically or semi-automatically. Second, to tackle the issue of large computation cost for successive manifold-ranking process when large video database is incorporated, we employ a pre-filtering process to filter out the majority of irrelevant samples while retaining the most relevant ones. And last, the manifold-ranking algorithm is used to explore the relationship among all of the rest samples based on the selected features. This scheme is extensible and flexible in terms of adding new features into the feature pool, introducing human interactions on selecting features, and defining new concepts.