Learning Binary Codes for Hashing via Feature Decomposition

Abstract

Binary code learning techniques have been actively studied for hashing based nearest neighbor search in recent years. However, most existing techniques directly map the data into a Hamming space, which ignores the inherent property that original features may lie in different subspaces. To address this issue, this paper proposes a novel method by learning binary codes on the latent components decomposed from the original features of the data. We assume that each latent component represents an underlying feature subspace and similar/dissimilar data may contain the same/different latent components. As a result, the decomposition step distinguishes the original data better and consequently improves the discriminative power of binary codes. The experimental results of image retrieval tasks carried out on the commonly used benchmark datasets show that the proposed method outperforms other state-of-the-art methods on generating similarity-preserving binary codes.