Explicit performance metric optimization for fusion-based video retrieval

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.