Light-weight spatial distribution embedding of adjacent features for image search

Abstract

Binary code embedding methods can effectively compensate the quantization error of bag-of-words (BoW) model and remarkably improve the image search performance. However, the existing embedding schemes commonly generate binary code by projecting local feature from original feature space into a compact binary space. The spatial relationship between the local feature and its neighbors are ignored. In this paper, we proposed two light-weight binary code embedding schemes, named content similarity embedding (CSE) and scale similarity embedding (SSE), to better balance the image search performance and re- source cost. Specially, the spatial distribution information for any local feature and its nearest neighbors are encoded into only several bits, which are used to verify the asserted matches of local features. The experimental results show that the proposed image search scheme achieves a better balance between image search performance and resource usage (i.e., time cost and memory usage).