High-Dimensional Sparse Cross-Modal Hashing with Fine-Grained Similarity Embedding

Hashing can compress heterogeneous high-dimensional data into compact binary codes while preserving the similarity to facilitate efficient retrieval and storage, and thus hashing has recently received much attention from information retrieval researchers. Most of the existing hashing methods first predefine a fixed length (e.g., 32, 64, or 128 bit) for the hash codes before learning them with this fixed length. However, one sample can be represented by various hash codes with different lengths, and thus there must be some associations and relationships among these different hash codes because they represent the same sample. Therefore, harnessing these relationships will boost the performance of hashing methods. Inspired by this possibility, in this study, we propose a new model jointly multiple hash learning (JMH), which can learn hash codes with multiple lengths simultaneously. In the proposed JMH method, three types of information are used for hash learning, which come from hash codes with different lengths, the original features of the samples and label. In contrast to the existing hashing methods, JMH can learn hash codes with different lengths in one step. Users can select appropriate hash codes for their retrieval tasks according to the requirements in terms of accuracy and complexity. To the best of our knowledge, JMH is one of the first attempts to learn multi-length hash codes simultaneously. In addition, in the proposed model, discrete and closed-form solutions for variables can be obtained by cyclic coordinate descent, thereby making the proposed model much faster during training. Extensive experiments were performed based on three benchmark datasets and the results demonstrated the superior performance of the proposed method.

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>

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.

An efficient dual semantic preserving hashing for cross-modal retrieval

Label consistent locally linear embedding based cross-modal hashing

Supervised discrete hashing (SDH) has widely attracted attention because it can directly obtain hash codes rather than relaxing the discrete constraint, which reduces information loss during the quantization process. However, SDH and its extensions tend to ignore the underlying geometry structure of data, while in many cases, data is lying on a low-dimensional manifold. Therefore, based on SDH, we propose a novel manifold learning-based supervised discrete hashing method in this paper to further improve the performance. Our goal is to learn compact hash codes by discovering latent information and using the local structure of data. To this end, we utilize the anchor graph to capture local neighborhood relationship embedding in data for manifold learning. Besides, since the previous methods use a strict binary label matrix for classification, we jointly optimize a relaxed label matrix to increase the discriminative ability of the proposed model. As such, the learned hash codes are more suitable for classification. Compared with SDH and other popular hashing methods, the experimental results on four large-scale datasets show that the proposed hashing method has better performance.

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.

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.

Hashing has been extensively utilized in cross-modal retrieval due to its high efficiency in handling large-scale, high-dimensional data. However, most existing cross-modal hashing methods operate as offline learning models, which learn hash codes in a batch-based manner and prove to be inefficient for streaming data. Recently, several online cross-modal hashing methods have been proposed to address the streaming data scenario. Nevertheless, these methods fail to fully leverage the semantic information and accurately optimize hashing in a discrete fashion. As a result, both the accuracy and efficiency of online cross-modal hashing methods are not ideal. To address these issues, this paper introduces the Semantic Embedding-based Online Cross-modal Hashing (SEOCH) method, which integrates semantic information exploitation and online learning into a unified framework. To exploit the semantic information, we map the semantic labels to a latent semantic space and construct a semantic similarity matrix to preserve the similarity between new data and existing data in the Hamming space. Moreover, we employ a discrete optimization strategy to enhance the efficiency of cross-modal retrieval for online hashing. Through extensive experiments on two publicly available multi-label datasets, we demonstrate the superiority of the SEOCH method.

Given a CNF formula F on n variables, the problem of model counting, also referred to as #SAT, is to compute the number of models or satisfying assignments of F. Recent years have witnessed a surge of effort towards developing efficient algorithmic techniques that combine the classical strongly 2-universal hash functions (from [Stockmeyer 1983]) with the remarkable progress in SAT solving over the past decade. These techniques augment the CNF formula F with random XOR constraints and invoke an NP oracle repeatedly on the resultant CNF-XOR formulas. In practice, the NP oracle calls are replaced by calls to a SAT solver and it is observed that runtime performance of modern SAT solvers (based on conflict-driven clause learning) on CNF-XOR formulas is adversely affected by the size of XOR constraints. The standard construction of 2-universal hash functions chooses every variable with probability p = 1/2 leading to XOR constraints of size n/2 in expectation. Consequently, the main challenge is to design sparse hash functions, where variables can be chosen with smaller probability and lead to smaller sized XOR constraints, which can then replace strongly 2-universal hash functions. In this paper, our goal is to address this challenge both from a theoretical and a practical perspective. First, we formalize a relaxation of universal hashing, called concentrated hashing, a notion implicit in prior works to design sparse hash functions. We then establish a novel and beautiful connection between concentration measures of these hash functions and isoperimetric inequalities on boolean hypercubes. This allows us to obtain tight bounds on variance as well as the dispersion index and show that p = O(log2 m/m) suffices for the design of sparse hash functions from {0, 1}n to {0, 1}m belonging to the concentrated hash family. Finally, we use sparse hash functions belonging to this concentrated hash family to develop new approximate counting algorithms. A comprehensive experimental evaluation of our algorithm on 1893 benchmarks demonstrates that the usage of sparse hash functions can lead to significant speedups. To the best of our knowledge, this work is the first study to demonstrate runtime improvement of approximate model counting algorithms through the usage of sparse hash functions, while still retaining strong theoretical guarantees (a la 2-universal hash functions).

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.

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.

Semantic hashing represents documents as compact binary vectors (hash codes) and allows both efficient and effective similarity search in large-scale information retrieval. The state of the art has primarily focused on learning hash codes that improve similarity search effectiveness, while assuming a brute-force linear scan strategy for searching over all the hash codes, even though much faster alternatives exist. One such alternative is multi-index hashing, an approach that constructs a smaller candidate set to search over, which depending on the distribution of the hash codes can lead to sub-linear search time. In this work, we propose Multi-Index Semantic Hashing (MISH), an unsupervised hashing model that learns hash codes that are both effective and highly efficient by being optimized for multi-index hashing. We derive novel training objectives, which enable to learn hash codes that reduce the candidate sets produced by multi-index hashing, while being end-to-end trainable. In fact, our proposed training objectives are model agnostic, i.e., not tied to how the hash codes are generated specifically in MISH, and are straight-forward to include in existing and future semantic hashing models. We experimentally compare MISH to state-of-the-art semantic hashing baselines in the task of document similarity search. We find that even though multi-index hashing also improves the efficiency of the baselines compared to a linear scan, they are still upwards of 33% slower than MISH, while MISH is still able to obtain state-of-the-art effectiveness.

With the increasing amount of multimedia data, cross-modality hashing has made great progress as it achieves sub-linear search time and low memory space. However, due to the huge discrepancy between different modalities, most existing cross-modality hashing methods cannot learn unified hash codes and functions for modalities at the same time. The gap between separated hash codes and functions further leads to bad search performance. In this paper, to address the issues above, we propose a novel end-to-end Deep Unified Cross-Modality Hashing method named DUCMH, which is able to jointly learn unified hash codes and unified hash functions by alternate learning and data alignment. Specifically, to reduce the discrepancy between image and text modalities, DUCMH utilizes data alignment to learn an auxiliary image to text mapping under the supervision of image-text pairs. For text data, hash codes can be obtained by unified hash functions, while for image data, DUCMH first maps images to texts by the auxiliary mapping, and then uses the mapped texts to obtain hash codes. DUCMH utilizes alternate learning to update unified hash codes and functions. Extensive experiments on three representative image-text datasets demonstrate the superiority of our DUCMH over several state-of-the-art cross-modality hashing methods.

NSDH: A Nonlinear Supervised Discrete Hashing framework for large-scale cross-modal retrieval