A simple but efficient way to combine VLAD with locality-constrained linear coding

Abstract

The VLAD (vector of locally aggregated descriptors) representation, derived from BoF and Fisher kernel, has shown its efficiency in the field of image search. However, assigning local descriptors to a codeword is a hard voting process, which does not consider the uncertainty and the plausibility for single codeword. In this paper, we propose an approach to combine VLAD with locality-constrained linear coding, as opposed to the original one, considering several nearest neighbors when assigning local descriptors and computing weights. In order to evaluate our proposed method, experiments are conducted on several image classification benchmarks, using VLAD for comparison. The experimental results show that our method stably outperforms VLAD in terms of classification accuracy, while producing feature representation of the same dimension without much additional computational cost.