Efficient Multi-resolution Histogram Matching for Bag-of-Features

Bag-of-features (BOF) derived from local visual features has recently been widely used in content based image classification and scene detection owing to their simplicity and good performance. However, the hyper-dimension of the BOF vector has limited its implementation in large scale datasets because of its high computation complexity. In this paper, we present a new strategy based on the multi-resolution structure of BOF vectors to gain a speed-up of matching. We construct the new structure in two different ways: the uniform quantization method and the non-uniform quantization method. The main idea is to build low level histograms according to the BOF vector. We also introduce the VA-file method in our approach to give an approximation limit in order to accelerate the searching speed of multi-resolution BOF candidate vectors. Experiments results show that our approach has made a great improvement in both efficiency and computational complexity than traditional BOF methods.