Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
A MFoM learning approach to robust multiclass multi-label text categorization
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Multimedia event detection with multimodal feature fusion and temporal concept localization
Machine Vision and Applications
Journal of Signal Processing Systems
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We present a learning framework for fusion-based video retrieval system, which explicitly optimizes given performance metrics. Real-world computer vision systems serve sophisticated user needs, and domain-specific performance metrics are used to monitor the success of such systems. However, the conventional approach for learning under such circumstances is to blindly minimize standard error rates and hope the targeted performance metrics improve, which is clearly suboptimal. In this work, a novel scheme to directly optimize such targeted performance metrics during learning is developed and presented. Our experimental results on two large consumer video archives are promising and showcase the benefits of the proposed approach.