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 ...

• Type: Conference
• Year: 2007
• Where: ICMCS
• Authors: Jelena Tesic, Apostol Natsev, Lexing Xie, John R. Smith
• Keywords: Direct Modeling, ICMCS 2007, Topic Examples, Visual Topics