SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
The relative effectiveness of concept-based versus content-based video retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Optimal multimodal fusion for multimedia data analysis
Proceedings of the 12th annual ACM international conference on Multimedia
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Query expansion using probabilistic local feedback with application to multimedia retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Searching consumer image collections using web-based concept expansion
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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This paper proposes an aggregative query generation which exploits a media document representation called feature term to create a query from multiple media examples, e.g. images. A feature term denotes an interval of one media feature dimension, such as a bin in colour histogram. This approach (1) can easily accumulate features from multiple query examples to generate an efficient query; (2) enables the exploration of text-based retrieval models for multimedia retrieval. Two criteria, minimised χ2 and maximised entropy, are proposed to optimise feature term selection. Two ranking functions, KL divergence and tf-idf based BM25 model, are used for relevance estimation. Experiments on the Corel photo collection demonstrate the effectiveness of feature terms.