Deep Unified Cross-Modality Hashing by Pairwise Data Alignment

An efficient dual semantic preserving hashing for cross-modal retrieval

Unsupervised cross-modal hashing has attracted considerable attention to support large-scale cross-modal retrieval. Although promising progresses have been made so far, existing methods still suffer from limited capability on excavating and preserving the intrinsic multi-modal semantics. In this paper, we propose a Correlation-Identity Reconstruction Hashing (CIRH) method to alleviate this challenging problem. We develop a new unsupervised deep cross-modal hash learning framework to model and preserve the heterogeneous multi-modal correlation semantics into both hash codes and functions, and simultaneously, we involve both the hash codes and functions with the descriptive identity semantics. Specifically, we construct a multi-modal collaborated graph to model the heterogeneous multi-modal correlations, and jointly perform the intra-modal and cross-modal semantic aggregation on homogeneous and heterogeneous graph networks to generate a multi-modal complementary representation with correlation reconstruction. Furthermore, an identity semantic reconstruction process is designed to involve the generated representation with identity semantics by reconstructing the input modality representations. Finally, we propose a correlation-identity consistent hash function learning strategy to transfer the modelled multi-modal semantics into the neural networks of modality-specific deep hash functions. Experiments demonstrate the superior performance of the proposed method on both retrieval accuracy and efficiency. We provide our source codes and experimental datasets at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XizeWu/CIRH</uri>

Deep hashing methods have achieved tremendous success in cross-modal retrieval, due to its low storage consumption and fast retrieval speed. Supervised cross-modal hashing methods have achieved substantial advancement by incorporating semantic information. However, to a great extent, supervised methods rely on large-scale labeled cross-modal training data which are laborious to obtain. Moreover, most cross-modal hashing methods only handle two modalities of image and text, without taking the scene of multiple modalities into consideration. In this paper, we propose a novel semi-supervised approach called semi-supervised knowledge distillation for cross-modal hashing (SKDCH) to overcome the above-mentioned challenges, which enables guiding a supervised method using outputs produced by a semi-supervised method for multimodality retrieval. Specifically, we utilize teacher-student optimization to propagate knowledge. Furthermore, we improves triplet ranking loss to better mitigate the heterogeneity gap, which increases the discriminability of our proposed approach. Extensive experiments executed on two benchmark datasets validate that the proposed SKDCH surpasses the state-of-the-art methods.

Cross-media hashing, which maps data from different modalities to a low-dimensional sharing Hamming space, has attracted considerable attention due to the rapid increase of multimodal data, for example, images and texts. Recent cross-media hashing works mainly aim at learning compact hash codes to preserve the class label-based or feature-based similarities among samples. However, these methods ignore the unbalanced semantic gaps between different modalities and high-level semantic concepts, which generally results in less effective hash functions and unsatisfying retrieval performance. Specifically, the key words of texts contain semantic meanings, while the low-level features of images lack of semantic meanings. That means the semantic gap in image modality is larger than that in text modality. In this paper, we propose a simple yet effective hashing method for cross-media retrieval to address this problem, dubbed discrete semantic alignment hashing (DSAH). First, DSAH formulates to exploit collaborative filtering to mine the relations between class labels and hash codes, which can reduce memory consumption and computational cost compared to pairwise similarity. Then, the attribute of image modality is employed to align the semantic information with text modality. Finally, to further improve the quality of hash codes, we propose a discrete optimization algorithm to learn discrete hash codes directly, and each bit has a closed-form solution. Extensive experiments on multiple public databases show that our model can seamlessly incorporate attributes and achieve promising performance.

Existing Cross Modal Hashing (CMH) methods are mainly designed for balanced data, while imbalanced data with long-tail distribution is more general in real-world. Several long-tail hashing methods have been proposed but they can not adapt for multi-modal data, due to the complex interplay between labels and individuality and commonality information of multi-modal data. Furthermore, CMH methods mostly mine the commonality of multi-modal data to learn hash codes, which may override tail labels encoded by the individuality of respective modalities. In this paper, we propose LtCMH (Long-tail CMH) to handle imbalanced multi-modal data. LtCMH firstly adopts auto-encoders to mine the individuality and commonality of different modalities by minimizing the dependency between the individuality of respective modalities and by enhancing the commonality of these modalities. Then it dynamically combines the individuality and commonality with direct features extracted from respective modalities to create meta features that enrich the representation of tail labels, and binaries meta features to generate hash codes. LtCMH significantly outperforms state-of-the-art baselines on long-tail datasets and holds a better (or comparable) performance on datasets with balanced labels.

Recently, with the discoveries in neurobiology, high-dimensional sparse hashing has attracted increasing attention. In contrast with general hashing that generates low-dimensional hash codes, the high-dimensional sparse hashing maps inputs into a higher dimensional space and generates sparse hash codes, achieving superior performance. However, the sparse hashing has not been fully studied in hashing literature yet. For example, how to fully explore the power of sparse coding in cross-modal retrieval tasks; how to discretely solve the binary and sparse constraints so as to avoid the quantization error problem. Motivated by these issues, in this paper, we present an efficient sparse hashing method, i.e., High-dimensional Sparse Cross-modal Hashing, HSCH for short. It not only takes the high-level semantic similarity of data into consideration, but also properly exploits the low-level feature similarity. In specific, we theoretically design a fine-grained similarity with two critical fusion rules. Then we take advantage of sparse codes to embed the fine-grained similarity into the to-be-learnt hash codes. Moreover, an efficient discrete optimization algorithm is proposed to solve the binary and sparse constraints, reducing the quantization error. In light of this, it becomes much more trainable, and the learnt hash codes are more discriminative. More importantly, the retrieval complexity of HSCH is as efficient as general hash methods. Extensive experiments on three widely-used datasets demonstrate the superior performance of HSCH compared with several state-of-the-art cross-modal hashing approaches.

Hashing has recently received great attention in cross-modal retrieval. Cross-modal retrieval aims at retrieving information across heterogeneous modalities (e.g., texts vs. images). Cross-modal hashing compresses heterogeneous high-dimensional data into compact binary codes with similarity preserving, which provides efficiency and facility in both retrieval and storage. In this study, we propose a novel fast discrete cross-modal hashing (FDCH) method with regressing from semantic labels to take advantage of supervised labels to improve retrieval performance. In contrast to existing methods that learn the projection from hash codes to semantic labels, the proposed FDCH regresses the semantic labels of training examples to the corresponding hash codes with a drift. It not only accelerates the hash learning process, but also helps generate stable hash codes. Furthermore, the drift can adjust the regression and enhance the discriminative capability of hash codes. Especially in the case of training efficiency, FDCH is much faster than existing methods. Comparisons with several state-of-the-art techniques on three benchmark datasets have demonstrated the superiority of FDCH under various cross-modal retrieval scenarios.

Due to the fast retrieval speed and low storage cost, cross-modal hashing has become the primary method for cross-modal retrieval. Since the emergence of deep cross-modal hashing methods, cross-modal retrieval significantly improved. However, the existing cross-modal hash retrieval methods still need to effectively utilize the dataset’s supervisory information and the lack of similarity expression ability. This means that the label information needs to be maximized, and the potential semantic relationship between two modalities cannot be fully explored, thus affecting the judgment of semantic similarity between two modalities. To address these problems, this paper proposes Tri-CMH, a cross-modal hash retrieval method with fused triples, which is an end-to-end modeling framework consisting of two parts: feature extraction and hash learning. Firstly, the multi-modal data are preprocessing into the form of triple groups. The data supervision matrix is constructed so that the samples with labels and their meanings are aggregated together. In contrast, the samples with labels and their opposite meanings are separated, thus avoiding the problem of the under-utilization of supervisory information in the data set and achieving the effect of efficiently utilizing the global supervisory information. Meanwhile, the loss function of the hash learning part is optimized by considering the Hamming distance loss, single-modality internal loss, cross-modality loss, and quantization loss to explicitly constrain semantically similar hash codes and semantically dissimilar hash codes and to improve the model’s ability to judge cross-modality semantic similarity. The method is trained and tested on the IAPR-TC12, MIRFLICKR-25K, and NUS-WIDE datasets, and the experimental evaluation criteria are mAP and PR curve, and the experimental results show the effectiveness and practicality of the method.

Benefitting from the low storage cost and high retrieval efficiency, hash learning has become a widely used retrieval technology to approximate nearest neighbors. Within it, the cross-modal medical hashing has attracted an increasing attention in facilitating efficiently clinical decision. However, there are still two main challenges in weak multi-manifold structure perseveration across multiple modalities and weak discriminability of hash code. Specifically, existing cross-modal hashing methods focus on pairwise relations within two modalities, and ignore underlying multi-manifold structures across over 2 modalities. Then, there is little consideration about discriminability, i.e., any pair of hash codes should be different. In this paper, we propose a novel hashing method named multi-manifold deep discriminative cross-modal hashing (MDDCH) for large-scale medical image retrieval. The key point is multi-modal manifold similarity which integrates multiple sub-manifolds defined on heterogeneous data to preserve correlation among instances, and it can be measured by three-step connection on corresponding hetero-manifold. Then, we propose discriminative item to make each hash code encoded by hash functions be different, which improves discriminative performance of hash code. Besides, we introduce Gaussian-binary Restricted Boltzmann Machine to directly output hash codes without using any continuous relaxation. Experiments on three benchmark datasets (AIBL, Brain and SPLP) show that our proposed MDDCH achieves comparative performance to recent state-of-the-art hashing methods. Additionally, diagnostic evaluation from professional physicians shows that all the retrieved medical images describe the same object and illness as the queried image.

Cross-modal hashing provides an efficient solution for retrieval tasks across various modalities, such as images and text. However, most existing methods are deterministic models, which overlook the reliability associated with the retrieved results. This omission renders them unreliable for determining matches between data pairs based solely on Hamming distance. To bridge the gap, in this paper, we propose a novel method called Deep Evidential Cross-modal Hashing (DECH). This method equips hashing models with the ability to quantify the reliability level of the association between a query sample and each corresponding retrieved sample, bringing a new dimension of reliability to the cross-modal retrieval process. To achieve this, our method addresses two key challenges: i) To leverage evidential theory in guiding the model to learn hash codes, we design a novel evidence acquisition module to collect evidence and place the evidence captured by hash codes on a Beta distribution to derive a binomial opinion. Unlike existing evidential learning approaches that rely on classifiers, our method collects evidence directly through hash codes. ii) To tackle the task-oriented challenge, we first introduce a method to update the derived binomial opinion, allowing it to present the uncertainty caused by conflicting evidence. Following this manner, we present a strategy to precisely evaluate the reliability level of retrieved results, culminating in performance improvement. We validate the efficacy of our DECH through extensive experimentation on four benchmark datasets. The experimental results demonstrate our superior performance compared to 12 state-of-the-art methods.

Cross-modal hashing has received widespread attentions on cross-modal retrieval task due to its superior retrieval efficiency and low storage cost. However, most existing cross-modal hashing methods learn binary codes directly from multimedia data, which cannot fully utilize the semantic knowledge of the data. Furthermore, they cannot learn the ranking based similarity relevance of data points with multi-label. And they usually use a relax constraint of hash code which causes non-negligible quantization loss in the optimization. In this paper, a hashing method called Deep Adversarial Discrete Hashing (DADH) is proposed to address these issues for cross-modal retrieval. The proposed method uses adversarial training to learn features across modalities and ensure the distribution consistency of feature representations across modalities. We also introduce a weighted cosine triplet constraint which can make full use of semantic knowledge from the multi-label to ensure the precise ranking relevance of item pairs. In addition, we use a discrete hashing strategy to learn the discrete binary codes without relaxation, by which the semantic knowledge from label in the hash codes can be preserved while the quantization loss can be minimized. Ablation experiments and comparison experiments on two cross-modal databases show that the proposed DADH improves the performance and outperforms several state-of-the-art hashing methods for cross-modal retrieval.

The booming demands for cross-modal retrieval tasks can often bring in its wake the development of retrieval technologies, as people turn to pursuing a more effective way to improve the performance of search results in both accuracy and efficiency, for example by unsupervised cross-modal hashing. It’s worth noting that most of the cross-modal hashing methods focus on utilizing merely one approach to generate hash codes. However, each approach has its own intrinsic drawback, which would inevitably diminish the quality of hash codes. In this paper, we propose a state-of-the-art model named Soft Constraint Hashing (SCH), using a special soft constraint term defined as an “information tunnel” to achieve the goal that conveys information from one approach to another. In particular, this “tunnel” can eliminate potential data noises to some extent and bridge the gap between two unsupervised discrete hashing allocation approaches to simultaneously reinforce the quality of hash codes. The empirical results on publicly available datasets illustrate that our proposed model outperforms all the existing unsupervised cross-model hashing methods.

Due to its computational efficiency and low storage requirement, cross-modal hashing (CMH) gains a lot of attention. However, there are still three issues that affect its performance: 1) most existing methods focus on learning the shared semantics between different modalities with the modality-specific semantics ignored; 2) noisy labels may further exacerbate the semantic differences of different modalities during learning; and 3) most existing methods overlook the complementarity between modality-specific labels and semantic features. To address these issues, this work develops a novel CMH method called Dual-Semantic Enhancement Cross-Modal Hashing with Noisy Labels (DSENL). DSENL consists of three parts: (a) Modality-Specific Label Recovery (MSLR) that obtains modality-specific clean labels by applying matrix decomposition with low-rank and sparse constraints to the observed labels; (b) Semantic Preservation under Label Guidance (SPLG) that enhances the quality of recovered labels by using an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{2,1}$</tex-math></inline-formula> norm and maintains semantic consistency across modalities by reducing discrepancies among modality-specific labels; and (c) Dual-Semantic Enhancement Learning (DSEL) that integrates both label and sample semantics from modality-specific to enhance the discriminative capability of hash codes. By DSENL, the discriminability of the learned hash codes is improved. Experimental results on four benchmark datasets demonstrate the effectiveness of DSENL. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/niuniubit/DSENL.git</uri>.

Label consistent locally linear embedding based cross-modal hashing

Cross-modal hashing methods have attracted considerable attention. Most pioneer approaches only preserve the neighborhood relationship by constructing the correlations among heterogeneous modalities. However, they neglect the fact that the high-dimensional data often exists on a low-dimensional manifold embedded in the ambient space and the relative proximity between the neighbors is also important. Although some methods leverage the manifold learning to generate the hash codes, most of them fail to explicitly explore the discriminative information in the class labels and discard the binary constraints during optimization, generating large quantization errors. To address these issues, in this paper, we present a novel cross-modal hashing method, named Supervised Discrete Manifold-Embedded Cross-Modal Hashing (SDMCH). It can not only exploit the non-linear manifold structure of data and construct the correlation among heterogeneous multiple modalities, but also fully utilize the semantic information. Moreover, the hash codes can be generated discretely by an iterative optimization algorithm, which can avoid the large quantization errors. Extensive experimental results on three benchmark datasets demonstrate that SDMCH outperforms ten state-of-the-art cross-modal hashing methods.