UCF @ TRECVID 2009 : High-Level Feature Extraction

This year, the University of Central Florida participated in the high level feature extraction task (HLF). The goal of high level feature extraction is to identify in videos specific shots that contain concepts such as “bus,” “person playing soccer,” and “boat/ship.” In our submissions, we focused on addressing the large imbalance between the positive and negative training examples. Specifically, we implemented a method called bootstrapping that identifies the best subset of negative examples to train on. In our experiments, we found bootstrapping significantly lowered the probability of false alarm while also improving the probability of detection. Additionally, we also explored different word weighting techniques. In the bag of words approach, certain words may be more discriminative than others; these words should be weighted more. This task served as a project for several students participating in the Research Experience for Undergraduates program (REU) at UCF.

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