Introduction to algorithms
Discriminative model fusion for semantic concept detection and annotation in video
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
The combination limit in multimedia retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic video annotation by semi-supervised learning with kernel density estimation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Probabilistic model supported rank aggregation for the semantic concept detection in video
Proceedings of the 6th ACM international conference on Image and video retrieval
Video diver: generic video indexing with diverse features
Proceedings of the international workshop on Workshop on multimedia information retrieval
Video annotation by graph-based learning with neighborhood similarity
Proceedings of the 15th international conference on Multimedia
AP-based borda voting method for feature extraction in TRECVID-2004
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News
IEEE Transactions on Multimedia
Social image annotation via cross-domain subspace learning
Multimedia Tools and Applications
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This paper studies the combination of video concept detectors with a labeled fusion set. We point out that the computational cost of the grid search for fusion weights increases exponentially with the number of detectors, and it is thus infeasible when dealing with a large number of detectors. To avoid the difficulty, we adopt incremental fusion approach, i.e., in each round two detectors are combined and hence only 1-dimensional grid search is needed. We propose a Bottom-Up Incremental Fusion (BUIF) method which keeps selecting the detectors with lowest performance for combination. We conduct experiments on TRECVID benchmark dataset for 39 concepts with 38 detection methods. Ten different fusion strategies are compared, and empirical results have demonstrated the superiority of the proposed incremental fusion approach.