Optimized Transform Coding for Approximate KNN Search

Transform coding (TC) is an efficient and effective vector quantization approach where the resulting compact representation can be the basis for a more elaborate hierarchical framework for sub-linear approximate search. However, as compared to the state-of-the-art product quantization methods, there is a significant performance gap in terms of matching accuracy. One of the main shortcomings of TC is that the solution for bit allocation relies on an assumption that probability density of each component of the vector can be made identical after normalization. Motivated by this, we propose an optimized transform coding (OTC) such that bit allocation is optimized directly on the binned kernel estimator of each component of the vector. Experiments on public datasets show that our optimized transform coding approach achieves performance comparable to the state-ofthe-art product quantization methods, while maintaining learning speed comparable to TC.