Artificial Intelligence
Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
Successful approaches in the TREC video retrieval evaluations
Proceedings of the 12th annual ACM international conference on Multimedia
Multimodal Video Indexing: A Review of the State-of-the-art
Multimedia Tools and Applications
Fusion of effective retrieval strategies in the same information retrieval system
Journal of the American Society for Information Science and Technology
The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using score distributions for query-time fusion in multimediaretrieval
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Estimating average precision with incomplete and imperfect judgments
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A hybrid framework for detecting the semantics of concepts and context
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
The use and utility of high-level semantic features in video retrieval
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
A probabilistic framework for semantic video indexing, filtering,and retrieval
IEEE Transactions on Multimedia
Extracting semantics from audio-visual content: the final frontier in multimedia retrieval
IEEE Transactions on Neural Networks
Foundations and Trends in Information Retrieval
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Recent successful approaches to high-level feature detection in image and video data have treated the problem as a pattern classification task. These typically leverage the techniques learned from statistical machine learning, coupled with ensemble architectures that create multiple feature detection models. Once created, co-occurrence between learned features can be captured to further boost performance. At multiple stages throughout these frameworks, various pieces of evidence can be fused together in order to boost performance. These approaches whilst very successful are computationally expensive, and depending on the task, require the use of significant computational resources. In this paper we propose two fusion methods that aim to combine the output of an initial basic statistical machine learning approach with a lower-quality information source, in order to gain diversity in the classified results whilst requiring only modest computing resources. Our approaches, validated experimentally on TRECVid data, are designed to be complementary to existing frameworks and can be regarded as possible replacements for the more computationally expensive combination strategies used elsewhere.