Utilizing related samples to learn complex queries in interactive concept-based video search
Proceedings of the ACM International Conference on Image and Video Retrieval
Simulating the future of concept-based video retrieval under improved detector performance
Multimedia Tools and Applications
Using visual lifelogs to automatically characterize everyday activities
Information Sciences: an International Journal
The uncertain representation ranking framework for concept-based video retrieval
Information Retrieval
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There is an increasing emphasis on including semantic concept detection as part of video retrieval. This represents a modality for retrieval quite different from metadata-based and keyframe similarity-based 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 beneficial? In this paper we explore this question using a collection of rushes video where we artificially 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 reflected as retrieval performance and this raises interesting questions about how accurate concept detection really needs to be.