Supervised learning based discrete hashing for image retrieval

Abstract

Learning based hashing technologies have been widely adopted in multimedia retrieval as they could afford efficient storage and extract semantic information for high-dimensional data. However, the major difficulty of learning based hashing is the discrete constraint imposed on the required hashing codes, which makes the optimization generally to be NP-hard. In this paper, a novel supervised learning based discrete hashing (SLDH) approach is proposed to learn compact binary codes under the deep learning framework for image retrieval. We adopt multilayer network to convert the original features into binary codes, while it should exploit the semantic relevance of manual labels and keep the semantic similarity. For this purpose, we propose the objective function to obtain the binary codes including: 1) making full use of manual labels to get implicit semantic information; 2) using the weighted similarity matrix to keep the semantic similarity; 3) relaxing the discrete constraint to a normalized optimization problem; 4) adding the orthogonality constraint on binary codes to reduce the information redundancy. The objective function is optimized with the alternating direction method and modified alternating direction of multipliers(ADMM) algorithm with efficient iteration. Experiments are conducted on three databases and the results demonstrate the superiority to several state-of-the-art hashing based image retrieval methods.