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Explicit Performance Metric Optimization for Fusion-Based Video Retrieval*
Ilseo Kim1, Sangmin Oh2, Byungki Byun3, A.G. Amitha Perera2, and Chin-Hui Lee1
1Georgia Institute of Technology, USA
2Kitware Inc., USA
3Microsoft, USA
Abstract. 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.
*This work was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center contract number D11PC20069. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DOI/NBC, or the U.S. Government.
LNCS 7585, p. 395 ff.
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