Supervised cross-modal hashing has attracted much attention. However, there are still some challenges, e.g., how to effectively embed the label information into binary codes, how to avoid using a large similarity matrix and make a model scalable to large-scale datasets, how to efficiently solve the binary optimization problem. To address these challenges, in this paper, we present a novel supervised cross-modal hashing method, i.e., scalaBle Asymmetric discreTe Cross-modal Hashing, BATCH for short. It leverages collective matrix factorization to learn a common latent space for the labels and different modalities, and embeds the labels into binary codes by minimizing a distance-distance difference problem. Furthermore, it builds a connection between the common latent space and the hash codes by an asymmetric strategy. In the light of this, it can perform cross-modal retrieval and embed more similarity information into the binary codes. In addition, it introduces a quantization minimization term and orthogonal constraints into the optimization problem, and generates the binary codes discretely. Therefore, the quantization error and redundancy may be much reduced. Moreover, it is a two-step method, making the optimization simple and scalable to large-scale datasets. Extensive experimental results on three benchmark datasets demonstrate that BATCH outperforms some state-of-the-art cross-modal hashing methods in terms of accuracy and efficiency.

In this paper, we present a novel supervised cross-modal hashing framework, namely, Scalable disCRete mATrix faCtorization Hashing, SCRATCH for short. It first utilizes Collective Matrix Factorization on original features, together with label semantic embedding, to learn the latent representations in a shared laten space. Thereafter, it generates binary hash codes based on the latent representations. During optimization, it avoids using a large n×n similarity matrix and generates hash codes discretely. Besides, based on different objective functions, learning strategy and features, we further present three models in this framework, i.e., SCRATCH-o, SCRATCH-t and SCRATCH-d. The first one is a one-step method, learning the hash functions and the binary codes in the same optimization problem. The second is a two-step method, which first generates the binary codes, and then learns the hash functions based on the learnt hash codes; The third one is a deep version of SCRATCH-t which utilizes deep neural networks as hash functions. Extensive experiments on two widely-used benchmark datasets demonstrate that SCRATCH-o and SCRATCH-t outperform some state-of-the-art shallow hashing methods for cross-modal retrieval. SCRATCH-d also outperforms some state-of-the-art deep hashing models.

Recently, supervised cross-modal hashing has attracted much attention and achieved promising performance. To learn hash functions and binary codes, most methods globally exploit the supervised information, for example, preserving an at-least-one pairwise similarity into hash codes or reconstructing the label matrix with binary codes. However, due to the hardness of the discrete optimization problem, they are usually time consuming on large-scale datasets. In addition, they neglect the class correlation in supervised information. From another point of view, they only explore the global similarity of data but overlook the local similarity hidden in the data distribution. To address these issues, we present an efficient supervised cross-modal hashing method, that is, fast cross-modal hashing (FCMH). It leverages not only global similarity information but also the local similarity in a group. Specifically, training samples are partitioned into groups; thereafter, the local similarity in each group is extracted. Moreover, the class correlation in labels is also exploited and embedded into the learning of binary codes. In addition, to solve the discrete optimization problem, we further propose an efficient discrete optimization algorithm with a well-designed group updating scheme, making its computational complexity linear to the size of the training set. In light of this, it is more efficient and scalable to large-scale datasets. Extensive experiments on three benchmark datasets demonstrate that FCMH outperforms some state-of-the-art cross-modal hashing approaches in terms of both retrieval accuracy and learning efficiency.

Supervised hashing methods have achieved more promising results than unsupervised ones by leveraging label information to generate compact and accurate hash codes. Most of the prior supervised hashing methods construct an n × n instance-pairwise similarity matrix, where n is the number of training samples. Nevertheless, this kind of similarity matrix results in high memory space cost and makes the optimization time-consuming, making it unacceptable in many real applications. In addition, most of the methods relax the discrete constraints to solve the optimization problem, which may cause large quantization errors and finally lead to poor performance. To address these limitations, in this paper, we present a novel hashing method, named discrete hashing with multiple supervision (MSDH). MSDH supervises the hash code learning with both class-wise and instance-class similarity matrices, whose space cost is much less than the instance-pairwise similarity matrix. With multiple supervision information, better hash codes can be learned. Besides, an iterative optimization algorithm is proposed to directly learn the discrete hash codes instead of relaxing the binary constraints. Experimental results on several widely used benchmark datasets demonstrate that MSDH outperforms some state-of-the-art methods.

In recent years, many achievements have been made in improving the performance of supervised cross-modal hashing. However, it remains an open issue on how to fully explore the data information to achieve fine-grained retrieval performance. Most methods employ logical labels or a binary similarity matrix to supervise the hash learning, losing a lot of useful information. From another point of view, the low expressiveness of dense hash code severely limits its preservation of fine-grained data information. With this motivation, in this paper, we propose a high-dimensional sparse hashing framework for cross-modal retrieval, i.e., High-dimensional Sparse Cross-modal Hashing, HSCH for short. It leverages not only high-level semantic labels but also low-level multi-modal features to construct a fine-grained similarity. In particular, based on two well-designed rules, i.e., multi-level and prioritized, it is able to avoid semantic conflicts. Additionally, it leverages the strong power of high-dimensional sparse hash codes to preserve the fine-grained similarity. Then, it efficiently solves the sparse and discrete constraints of sparse hash codes through an efficient discrete optimization algorithm. In light of this, it is much more efficient and scalable to large-scale datasets. More importantly, the computational complexity of HSCH in the retrieval phase is as efficient as those naive hashing methods that use dense hash codes. Moreover, to support online learning scenarios, this paper also extends HSCH into an online version, i.e., HSCH_on. Extensive experiments on three benchmark datasets demonstrate the superiority of our framework compared with some state-of-the-art cross-modal hashing approaches in terms of both accuracy and efficiency.

Fine-grained hashing is a new topic in the field of hashing-based retrieval and has not been well explored up to now. In this paper, we raise three key issues that fine-grained hashing should address simultaneously, i.e., fine-grained feature extraction, feature refinement as well as a well-designed loss function. In order to address these issues, we propose a novel Fine-graIned haSHing method with a double-filtering mechanism and a proxy-based loss function, FISH for short. Specifically, the double-filtering mechanism consists of two modules, i.e., Space Filtering module and Feature Filtering module, which address the fine-grained feature extraction and feature refinement issues, respectively. Thereinto, the Space Filtering module is designed to highlight the critical regions in images and help the model to capture more subtle and discriminative details; the Feature Filtering module is the key of FISH and aims to further refine extracted features by supervised re- weighting and enhancing. Moreover, the proxy-based loss is adopted to train the model by preserving similarity relationships between data instances and proxy-vectors of each class rather than other data instances, further making FISH much efficient and effective. Experimental results demonstrate that FISH achieves much better retrieval performance compared with state-of-the-art fine-grained hashing methods, and converges very fast. The source code is publicly available: https://github.com/chenzhenduo/FISH .