Data modeling strategies for imbalanced learning in visual search

In this paper we examine a novel approach to the difficult problem of querying video databases using visual topics with few examples. Typically with visual topics, the examples are not sufficiently diverse to create a robust model of the user's need. As a result, direct modeling using the provided topic examples as training data is inadequate. Otherwise, systems resort to multiple content-based searches using each example in turn, which typically provides poor results. We propose a new technique of leveraging unlabeled data to expand the diversity of the topic examples as well as provide a robust set of negative examples that allow direct modeling. The approach intelligently models a pseudo-negative space using unbiased and biased methods for data sampling and data selection. We apply the proposed method in a fusion framework to improve discriminative support vector machine modeling, and improve the overall system performance. The result is an enhanced performance over any of the baseline models, as well as improved robustness with respect to training examples, visual features, and visual support of video topics in TRECVID. The proposed method outperforms a baseline retrieval approach by more than 18% on the TRECVID 2006 video collection and query topics.