An adaptive classification method for multimedia retrieval

Relevance feedback can effectively improve the performance of content-based multimedia retrieval systems. To be effective, a relevance feedback approach must be able to efficiently capture the user's query concept from a very limited number of training samples. To address this issue, we propose a novel adaptive classification method using random forests, which is a machine learning algorithm with proven good performance on many traditional classification problems. With random forests, our method reduces the relevance feedback to a two-class classification problem and classifies database objects as relevant or irrelevant. From the relevant object set, our approach returns the top k nearest neighbors of the query to the user. Briefly speaking, our relevance feedback method has the following dominant features. First, our method is able to address the multimodal distribution of relevant points, because it trains a nonparametric and nonlinear classifier, i.e., random forests, for relevance feedback. Second, it does not overfit training data because it uses an ensemble of tree classifiers to classify multimedia objects. Experiments on a Corel image set (with 31,438 images) show that our method significantly outperforms the state-of-the-art relevance feedback approaches.