A Probabilistic Ranking Framework using Unobservable Binary Events for Video Search

by Robin Aly, Djoerd Hiemstra, Arjen de Vries, and Franciska de Jong

CIVR 2008, Niagara Falls 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

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