Content-based Image Retrieval (CBIR) is a computer vision application that aims at automatically retrieving images based on their visual content. Linear Discriminat Analysis and its variants have been widely used in CBIR applications because of their effectiveness in finding a projection that maps the original highdimensional space to a low-dimensional one and preserves the most discriminant features. Those techniques assume images from certain class(es) are all visually similar and try to cluster them in the projected space. In this paper we show that the human high-level concept of semantic similarity between images may not arise only from the low-level visual similarity and consequently that assumption is inappropriate in many cases. We propose an Adaptive Discrimant Projection (ADP) framework which could model different data distributions based on the clustering of different classes. To learn the best model fitting the real scenario, Boosted Adaptive Discriminant Projection is further proposed. Extensive experiments are designed to evaluate our methods and compare them to the state-of-the-art techniques on benchmark data set and real image retrieval applications. The results show the superior performance of our proposed methods.
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