Measuring the Influence of Concept Detection on Video Retrieval

There is an increasing emphasis on including semantic concept detection as part of video retrieval. This represents a modality for retrieval quite di erent from metadata-based and keyframe similaritybased approaches. One of the premises on which the success of this is based, is that good quality detection is available in order to guarantee retrieval quality. But how good does the feature detection actually need to be? Is it possible to achieve good retrieval quality, even with poor quality concept detection and if so then what is the tipping point below which detection accuracy proves not to be bene cial? In this paper we explore this question using a collection of rushes video where we arti cially vary the quality of detection of semantic features and we study the impact on the resulting retrieval. Our results show that the impact of improving or degrading performance of concept detectors is not directly re ected as retrieval performance and this raises interesting questions about how accurate concept detection really needs to be.