Comparing discriminating transformations and SVM for learning during multimedia retrieval

On-line learning or "relevance feedback" techniques for multimedia information retrieval have been explored from many different points of view: from early heuristic-based feature weighting schemes to recently proposed optimal learning algorithms, probabilistic/Bayesian learning algorithms, boosting techniques, discriminant-EM algorithm, support vector machine, and other kernel-based learning machines. Based on a careful examination of the problem and a detailed analysis of the existing solutions, we propose several discriminating transforms as the learning machine during the user interaction. We argue that relevance feedback problem is best represented as a biased classification problem, or a (1+x)-class classification problem. Biased Discriminant Transform (BDT) is shown to outperform all the others. A kernel form is proposed to capture non-linearity in the class distributions.

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