Angular Quantization Online Hashing for Image Retrieval

Abstract

Online hash method with fast search mechanism and compact index structure plays a pivotal role. The inner product between label data has become one of the important means to measure the similarity between existing data and new data streams in online hashing methods. However, due to its discrete attributes and semantic gap, it often leads to a large amount of information loss. In this article, we propose a novel method called Angular Quantization Online Hashing (AQOH) to focus on learning compact binary codes with the help of cosine distance. Specifically, we propose an online hashing method for angular quantization, by minimizing the quantization error between the cosine similarity calculated from the original data and the generated binary code between the existing data and the new data stream. Further, within this framework, two effective algorithms to complete the optimization of the objective function to be designed, including continuous and discrete methods, respectively. Extensive experiments on various benchmark databases for online retrieval verify that our method outperforms many state-of-the art learning to hash methods.