In this paper, we describe the use of a Boosting algorithm, Real AdaBoost, for Content-Based Image Retrieval (CBIR) on a large number (190) of keyword categories. Previous work with Boosting for image orientation detection has involved only a few categories, such as a simple outdoor vs. indoor scene dichotomy. Other work with CBIR has incorporated Boosting into relevance feedback for a form of supervised learning based on end-users' evaluation, but here we use AdaBoost as a purely learning algorithm to reduce noisy and outlier information. For the 190-category classification task, Real AdaBoost with its own final learner model outperformed the K-Nearest Neighbour (K-NN) classifier in terms of precision.
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