A Probabilistic Ranking Framework using Unobservable Binary Events for Video Search
by Robin Aly, Djoerd Hiemstra, Arjen de Vries, and Franciska de Jong
This paper concerns the problem of search using the output of
concept detectors (also known as high-level features) for
video retrieval.
Unlike term occurrence in text documents, the event of the occurrence
of an audiovisual concept is only indirectly observable. We
develop a probabilistic ranking framework for unobservable binary
events to search in videos, called PR-FUBE. The framework explicitly
models the probability of relevance of a video shot through
the presence and absence of concepts. From our framework, we
derive a ranking formula and show its relationship to previously
proposed formulas. We evaluate our framework against two other
retrieval approaches using the TRECVID 2005 and 2007 datasets.
Especially using large numbers of concepts for retrieval results in
good performance. We attribute the observed robustness against
the noise introduced by less related concepts to the effective combination
of concept presence and absence in our method. The experiments
show that an accurate estimate for the probability of occurrence
of a particular concept in relevant shots is crucial to obtain
effective retrieval results.
The paper will be presented at the ACM International Conference on Image and Video Retrieval CIVR 2008 in Niagara Falls, Canada