Supervised Robust Discrete Multimodal Hashing for Cross-Media Retrieval

Abstract

The hashing-based approximate nearest neighbors search is able to reduce the storage cost and improve query speed. Therefore, they have attracted much attention in these years. Moreover, some hashing methods have been proposed for cross-modal retrieval tasks. However, there are still some issues that need to be further addressed. For example, some of them only construct a simple similarity matrix when learning hash functions or binary codes, which may lose some useful information. Some of them solve the hard discrete optimization problem by relaxing the binary constraints and quantizing the solution to obtain the final results, which may generate large quantization errors. To address these challenges, we present a new supervised cross-modal hashing method, named supervised robust discrete multimodal hashing (SRDMH). Specifically, it incorporates full label information into the hash functions learning to preserve the similarity in the original space. In addition, instead of relaxing the binary constraints, it is able to learn the binary codes and hash functions simultaneously. Moreover, it adopts a flexible ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,p</sub> loss with nonlinear kernel embedding and introduces an intermediate presentation of the binary codes. In light of this, it becomes more robust and easier to solve by an iterative algorithm presented in this paper. To evaluate its performance, we conduct extensive experiments on three benchmark datasets. The results verify that SRDMH outperforms seven state-of-the-art cross-modal hashing methods. In addition, we also extend it to the classification task. Compared with other hashing methods, SRDMH also obtains better results when its binary codes are used for classification.