Recent content-based video retrieval systems combine output of
concept detectors (also known as high-level features) with text
obtained through automatic speech recognition. This paper concerns
the problem of search using the noisy concept detector output only.
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 in 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.
|Conference||7th ACM International Conference on Content-based Image and Video Retrieval, CIVR 2008|
|Period||7/07/08 → 9/07/08|
|Other||7-9 July 2008|