Regional Subspace Projection Coding for Image Retrieval

Mingmin Zhen, Wenmin Wang, Ronggang Wang

Abstract: For image retrieval task, hamming embedding, being proved to be one of the state-of-the-art methods, has been prevalently utilised. The basic idea is to project local features into orthogonal space randomly, in which the binary signature is generated based on a single partition of feature space. However, the binary signature generation process is coarse and heuristic. On the one hand, the same projection is carried out for all visual word space without consideration of difference among subspaces. On the other hand, the projection matrix is generated randomly regardless of the distribution of feature data. Therefore, the performance of hamming embedding is limited and far from the optimal. In this paper, we firstly analyse the limitation of hamming em- bedding and compare different orthogonal projection methods. Then we propose a regional subspace projection coding method that is based on the distribution of local features assigned to each visual word. Finally, our experiments on two benchmark datasets demonstrate that our proposed method outperforms current state-of-the-art methods.