The state-of-the-art content based image retrieval systems has been significantly advanced by the introduction of SIFT features and the bag-of-words image representation. Converting an image into a bag-of-words, however, involves three non-trivial steps: feature detection, feature description, and feature quantization. At each of these steps, there is a significant amount of information lost, and the resulted visual words are often not discriminative enough for large scale image retrieval applications. In this paper, we propose a novel multi-sample multi-tree approach to computing the visual word codebook. By encoding more information of the original image feature, our approach generates a much more discriminative visual word codebook that is also efficient in terms of both computation and space consumption, without losing the original repeatability of the visual features. We evaluate our approach using both a ground-truth data set and a real-world large scale image database. Our results show that a significant improvement in both precision and recall can be achieved by using the codebook derived from our approach.
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