Biblio

Recently, some cross-modal hashing methods have been devised for cross-modal search task. Essentially, given a similarity matrix, most of these methods tackle a discrete optimization problem by separating it into two stages, i.e., first relaxing the binary constraints and finding a solution of the relaxed optimization problem, then quantizing the solution to obtain the binary codes. This scheme will generate large quantization error. Some discrete optimization methods have been proposed to tackle this; however, the generation of the binary codes is independent of the features in the original space, which makes it not robust to noise. To consider these problems, in this paper, we propose a novel supervised cross-modal hashing method—Semi-Relaxation Supervised Hashing (SRSH). It can learn the hash functions and the binary codes simultaneously. At the same time, to tackle the optimization problem, it relaxes a part of binary constraints, instead of all of them, by introducing an intermediate representation variable. By doing this, the quantization error can be reduced and the optimization problem can also be easily solved by an iterative algorithm proposed in this paper. Extensive experimental results on three benchmark datasets demonstrate that SRSH can obtain competitive results and outperform state-of-the-art unsupervised and supervised cross-modal hashing methods.

Recently, multimodal hashing techniques have received considerable attention due to their low storage cost and fast query speed for multimodal data retrieval. Many methods have been proposed; however, there are still some problems that need to be further considered. For example, some of these methods just use a similarity matrix for learning hash functions which will discard some useful information contained in original data; some of them relax binary constraints or separate the process of learning hash functions and binary codes into two independent stages to bypass the obstacle of handling the discrete constraints on binary codes for optimization, which may generate large quantization error; some of them are not robust to noise. All these problems may degrade the performance of a model. To consider these problems, in this paper, we propose a novel supervised hashing framework for cross-modal retrieval, i.e., Supervised Robust Discrete Multimodal Hashing (SRDMH). Specifically, SRDMH tries to make final binary codes preserve label information as same as that in original data so that it can leverage more label information to supervise the binary codes learning. In addition, it learns hashing functions and binary codes directly instead of relaxing the binary constraints so as to avoid large quantization error problem. Moreover, to make it robust and easy to solve, we further integrate a flexible l2,p loss with nonlinear kernel embedding and an intermediate presentation of each instance. Finally, an alternating algorithm is proposed to solve the optimization problem in SRDMH. Extensive experiments are conducted on three benchmark data sets. The results demonstrate that the proposed method (SRDMH) outperforms or is comparable to several state-of-the-art methods for cross-modal retrieval task.