Imbalanced RankBoost for efficiently ranking large-scale image/video collections

Ranking large scale image and video collections usually expects higher accuracy on top ranked data, while tolerates lower accuracy on bottom ranked ones. In view of this, we propose a rank learning algorithm, called Imbalanced RankBoost, which merges RankBoost and iterative thresholding into a unified loss optimization framework. The proposed approach provides a more efficient ranking process by iteratively identifying a cutoff threshold in each boosting iteration, and automatically truncating ranking feature computation for the data ranked below. Experiments on the TRECVID 2007 high-level feature benchmark show that the proposed approach outperforms RankBoost in terms of both ranking effectiveness and efficiency. It achieves an up to 21% improvement in terms of mean average precision, or equivalently, a 6-fold speedup in the ranking process.