Dimensionality reduction of visual features using sparse projectors for content-based image retrieval

In web-scale image retrieval, the most effective strategy is to aggregate local descriptors into a high dimensionality signature and then reduce it to a small dimensionality. Thanks to this strategy, web-scale image databases can be represented with small index and explored using fast visual similarities. However, the computation of this index has a very high complexity, because of the high dimensionality of signature projectors. In this work, we propose a new efficient method to greatly reduce the signature dimensionality with low computational and storage costs. Our method is based on the linear projection of the signature onto a small subspace using a sparse projection matrix. We report several experimental results on two standard datasets (Inria Holidays and Oxford) and with 100k image distractors. We show that our method reduces both the projectors storage cost and the computational cost of projection step while incurring a very slight loss in mAP (mean Average Precision) performance of these computed signatures.