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 groundtruth

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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In: The 12th International Conference on Computer Vision (ICCV)

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