Probabilistic models in information retrieval
The Computer Journal - Special issue on information retrieval
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic model of information retrieval: development and comparative experiments Part 2
Information Processing and Management: an International Journal
A survey on the use of relevance feedback for information access systems
The Knowledge Engineering Review
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Probabilistic models for combining diverse knowledge sources in multimedia retrieval
Probabilistic models for combining diverse knowledge sources in multimedia retrieval
The state of the art in image and video retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Using high-level semantic features in video retrieval
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
The generalized distributive law
IEEE Transactions on Information Theory
Concept detectors: how good is good enough?
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Reusing annotation labor for concept selection
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
The uncertain representation ranking framework for concept-based video retrieval
Information Retrieval
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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.