Unsupervised Hashing Methods Research Articles

Graph-Collaborated Auto-Encoder Hashing for Multiview Binary Clustering.

Unsupervised hashing methods have attracted widespread attention with the explosive growth of large-scale data, which can greatly reduce storage and computation by learning compact binary codes. Existing unsupervised hashing methods attempt to exploit the valuable information from samples, which fails to take the local geometric structure of unlabeled samples into consideration. Moreover, hashing based on auto-encoders aims to minimize the reconstruction loss between the input data and binary codes, which ignores the potential consistency and complementarity of multiple sources data. To address the above issues, we propose a hashing algorithm based on auto-encoders for multiview binary clustering, which dynamically learns affinity graphs with low-rank constraints and adopts collaboratively learning between auto-encoders and affinity graphs to learn a unified binary code, called graph-collaborated auto-encoder (GCAE) hashing for multiview binary clustering. Specifically, we propose a multiview affinity graphs' learning model with low-rank constraint, which can mine the underlying geometric information from multiview data. Then, we design an encoder-decoder paradigm to collaborate the multiple affinity graphs, which can learn a unified binary code effectively. Notably, we impose the decorrelation and code balance constraints on binary codes to reduce the quantization errors. Finally, we use an alternating iterative optimization scheme to obtain the multiview clustering results. Extensive experimental results on five public datasets are provided to reveal the effectiveness of the algorithm and its superior performance over other state-of-the-art alternatives.

HARR: Learning Discriminative and High-Quality Hash Codes for Image Retrieval

This article studies deep unsupervised hashing, which has attracted increasing attention in large-scale image retrieval. The majority of recent approaches usually reconstruct semantic similarity information, which then guides the hash code learning. However, they still fail to achieve satisfactory performance in reality for two reasons. On the one hand, without accurate supervised information, these methods usually fail to produce independent and robust hash codes with semantics information well preserved, which may hinder effective image retrieval. On the other hand, due to discrete constraints, how to effectively optimize the hashing network in an end-to-end manner with small quantization errors remains a problem. To address these difficulties, we propose a novel unsupervised hashing method called HARR to learn discriminative and high-quality hash codes. To comprehensively explore semantic similarity structure, HARR adopts the Winner-Take-All hash to model the similarity structure. Then similarity-preserving hash codes are learned under the reliable guidance of the reconstructed similarity structure. Additionally, we improve the quality of hash codes by a bit correlation reduction module, which forces the cross-correlation matrix between a batch of hash codes under different augmentations to approach the identity matrix. In this way, the generated hash bits are expected to be invariant to disturbances with minimal redundancy, which can be further interpreted as an instantiation of the information bottleneck principle. Finally, for effective hashing network training, we minimize the cosine distances between real-value network outputs and their binary codes for small quantization errors. Extensive experiments demonstrate the effectiveness of our proposed HARR.

Contrast-based unsupervised hashing method with margin limit

Contrast-based unsupervised hashing method with margin limit

Unsupervised Hashing Retrieval via Efficient Correlation Distillation

Deep hashing has been widely used in multimedia retrieval systems due to its storage and computation efficiency. Unsupervised hashing has received a lot of attention in recent years because it does not rely on label information. However, existing deep unsupervised hashing methods usually use rough pairwise relations to constrain the similarity between hash codes locally, which is insufficient and inefficient to reconstruct accurate correlations across samples. To address this issue, we propose a generic distillation framework for the preservation of the similarity relationship. Specifically, we design a distillation loss to reconstruct the batchwise similarity distribution between feature space and hash code space, allowing us to capture the global correlation knowledge contained in features and propagate it into hash codes efficiently. This framework can apply to both intra-modal and inter-modal scenarios. Furthermore, we design a new quantization method that quantizes the continuous values to a clipping value instead of ±1 to reduce the inconsistency between continuous features and hash codes. This method can also avoid the vanishing gradient problem during training. Finally, extensive experiments for image hashing retrieval and cross-modal hashing retrieval on public datasets demonstrate that the proposed method can yield compact hash codes and outperforms the state-of-the-art baselines.

Unsupervised Hashing with Semantic Concept Mining

Recently, to improve the unsupervised image retrieval performance, plenty of unsupervised hashing methods have been proposed by designing a semantic similarity matrix, which is based on the similarities between image features extracted by a pre-trained CNN model. However, most of these methods tend to ignore high-level abstract semantic concepts contained in images. Intuitively, concepts play an important role in calculating the similarity among images. In real-world scenarios, each image is associated with some concepts, and the similarity between two images will be larger if they share more identical concepts. Inspired by the above intuition, in this work, we propose a novel Unsupervised Hashing with Semantic Concept Mining, called UHSCM, which leverages a VLP model to construct a high-quality similarity matrix. Specifically, a set of randomly chosen concepts is first collected. Then, by employing a vision-language pretraining (VLP) model with the prompt engineering which has shown strong power in visual representation learning, the set of concepts is denoised according to the training images. Next, the proposed method UHSCM applies the VLP model with prompting again to mine the concept distribution of each image and construct a high-quality semantic similarity matrix based on the mined concept distributions. Finally, with the semantic similarity matrix as guiding information, a novel hashing loss with a modified contrastive loss based regularization item is proposed to optimize the hashing network. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task.

Deep debiased contrastive hashing

Hashing has achieved great success in multimedia retrieval due to its high computing efficiency and low storage cost. Recently, contrastive-learning-based hashing methods have achieved decent retrieval performance in label-free scenarios by learning distortion-invariant representations with Siamese networks. Their learning principle, i.e., instance discrimination, maximizes the correlation between self-augmented views and treats all others as negative samples. However, it may learn with false negative samples that are naturally similar, resulting in biased hash learning. To bridge this flaw, we reveal the between-instance similarity of naturally similar samples by exploring the latent structure of the training data. As a result, we propose the Deep Debiased Contrastive Hashing (DDCH) algorithm, using the neighborhood discovery module to explore the intrinsic similarity relationship that can help contrastive hashing reduce false negatives for superior discriminatory ability. Furthermore, we elucidate the rationale for incorporating the module into the contrastive hashing framework and explain our hashing process from an Expectation-Maximization (EM) perspective. Extensive experimental results on three benchmark image datasets demonstrate that DDCH significantly outperforms the state-of-the-art unsupervised hashing methods for image retrieval.

Domain Adaptation Preconceived Hashing for Unconstrained Visual Retrieval.

Learning to hash has been widely applied for image retrieval due to the low storage and high retrieval efficiency. Existing hashing methods assume that the distributions of the retrieval pool (i.e., the data sets being retrieved) and the query data are similar, which, however, cannot truly reflect the real-world condition due to the unconstrained visual cues, such as illumination, pose, background, and so on. Due to the large distribution gap between the retrieval pool and the query set, the performances of traditional hashing methods are seriously degraded. Therefore, we first propose a new efficient but transferable hashing model for unconstrained cross-domain visual retrieval, in which the retrieval pool and the query sample are drawn from different but semantic relevant domains. Specifically, we propose a simple yet effective unsupervised hashing method, domain adaptation preconceived hashing (DAPH), toward learning domain-invariant hashing representation. Three merits of DAPH are observed: 1) to the best of our knowledge, we first propose unconstrained visual retrieval by introducing DA into hashing for learning transferable hashing codes; 2) a domain-invariant feature transformation with marginal discrepancy distance minimization and feature reconstruction constraint is learned, such that the hashing code is not only domain adaptive but content preserved; and 3) a DA preconceived quantization loss is proposed, which further guarantees the discrimination of the learned hashing code for sample retrieval. Extensive experiments on various benchmark data sets verify that our DAPH outperforms many state-of-the-art hashing methods toward unconstrained (unrestricted) instance retrieval in both single- and cross-domain scenarios.

Deep Unsupervised Weighted Hashing for Remote Sensing Image Retrieval

Deep unsupervised hashing methods are gaining attention in the field of remote sensing (RS) image retrieval due to the rapid growth in the volume of unlabeled RS data. Most previous unsupervised hashing research used only natural image-based pre-trained models to generate label matrices; however, this method cannot capture the semantic information of RS images well and limits the accuracy of retrieval. To solve this problem, the authors propose a deep unsupervised weighted hashing (DUWH) model that uses a similarity matrix updating strategy based on a weighted similarity structure to achieve the mutual optimization of the similarity matrix and hash network. The authors devise a novel combinatorial loss function to improve the hash performance that can be used to obtain higher quality hash codes by assigning different weights to the sample pairs with different difficulties. Experiments were conducted on two RS datasets to verify the excellent performance of the proposed method.

Deep Contrastive Self-Supervised Hashing for Remote Sensing Image Retrieval

Hashing has been widely used for large-scale remote sensing image retrieval due to its outstanding advantages in storage and search speed. Recently, deep hashing methods, which produce discriminative hash codes by building end-to-end deep convolutional networks, have shown promising results. However, training these networks requires numerous labeled images, which are scarce and expensive in remote sensing datasets. In order to solve this problem, we propose a deep unsupervised hashing method, namely deep contrastive self-supervised hashing (DCSH), which uses only unlabeled images to learn accurate hash codes. It eliminates the need for label annotation by maximizing the consistency of different views generated from the same image. More specifically, we assume that the hash codes generated from different views of the same image are similar, and those generated from different images are dissimilar. On the basis of the hypothesis, we can develop a novel loss function containing the temperature-scaled cross-entropy loss and the quantization loss to train the developed deep network end-to-end, resulting in hash codes with semantic similarity preserved. Our proposed network contains four parts. First, each image is transformed into two different views using data augmentation. After that, they are fed into an encoder with the same shared parameters to obtain deep discriminate features. Following this, a hash layer converts the high-dimensional image representations into compact binary codes. Lastly, a novel hash function is introduced to train the proposed network end-to-end and thus guide generated hash codes with semantic similarity. Extensive experiments on two popular benchmark datasets of the UC Merced Land Use Database and the Aerial Image Dataset have demonstrated that our DCSH has significant superiority in remote sensing image retrieval compared with state-of-the-art unsupervised hashing methods.

Unsupervised Deep Pairwise Hashing

Although unsupervised deep hashing is potentially very useful for tackling many large-scale tasks, its performance is still far below satisfactory. Additionally, its performance might be significantly improved by effectively exploiting the pair similarity relationship among training data, but the attained similarity matrix usually contains noisy information, which often largely decreases the model performance. To alleviate this issue, in this paper, we propose a novel unsupervised deep pairwise hashing method to effectively and robustly exploit the similarity information between training samples and multiple anchors. We first create an ensemble anchor-based pairwise similarity matrix to enhance the robustness of similarity and dissimilarity relations between training samples and anchors. Afterwards, we propose a novel loss function to directly and robustly take advantage of the similarity and dissimilarity information via a weighted cross-entropy loss, and make use of a square loss to reduce the gap between latent binary vectors and binary codes, and another square loss to form consensus predictions of latent binary vectors. Extensive experiments on large-scale benchmark databases demonstrate the effectiveness of the proposed method, which outperforms recent state-of-the-art unsupervised hashing methods with significantly better ranking performance.

Improve Deep Unsupervised Hashing via Structural and Intrinsic Similarity Learning

Hashing has attracted increasing attention in image retrieval recently due to its storage and computational efficiency. Although several deep unsupervised hashing methods have been proposed lately, their effectiveness is far from satisfactory in practice owing to two drawbacks. On the one hand, they mostly construct binary similarity matrices which could neglect the confidence differences among multiple similarity signals. On the other hand, they ignore the desired properties of hash codes (i.e., independence and robustness). In this paper, we propose an effective unsupervised hashing method called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> ashing via <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> tructural and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> ntrinsic si <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ilarity learning (HashSIM) to tackle these issues in an end-to-end manner. Specifically, HashSIM utilizes both highly and normally confident image pairs to jointly build a continuous similarity matrix, which guides hash code learning via structural similarity learning. Moreover, inspired by contrastive learning, we impose an intrinsic similarity learning objective, which can maximally satisfy the independence and robustness properties of hash bits. Extensive experiments on three popular benchmark datasets demonstrate that our HashSIM outperforms a broad range of state-of-the-art baselines.

Efficient Semi-Supervised Multimodal Hashing With Importance Differentiation Regression.

Multi-modal hashing learns compact binary hash codes by collaborating heterogeneous multi-modal features at both the model training and online retrieval stages to support large-scale multimedia retrieval. Previous multi-modal hashing methods mainly focus on supervised and unsupervised hashing. The performance of supervised hashing largely relies on the number of labeled data, which is practically expensive to obtain. Unsupervised hashing methods cannot effectively capture the semantic correlations of multi-modal data without any labels for supervision. In this paper, we propose an Efficient Semi-supervised Multi-modal Hashing with Importance Differentiation Regression (ESMH-IDR) model, which can alleviate the existing problems by learning from both labeled and unlabeled data. Specifically, in this paper, we develop an efficient semi-supervised multi-modal hash code learning module. It learns the hash codes for labeled data in an efficient asymmetric way, and simultaneously performs nonlinear regression using the same projection matrix as the labeled samples to preserve the intrinsic data structure of unlabeled data. Besides, different from existing methods, we propose an importance differentiation regression strategy to learn hash functions by specially considering the different importance of hash codes learned from the labeled and unlabeled samples. Finally, we develop an efficient discrete optimization method guaranteed with convergence to iteratively solve the hash optimization problem. Experiments on several public multimedia retrieval datasets demonstrate the superiority of our proposed method on both retrieval effectiveness and efficiency. Our source codes and testing datasets can be obtained at https://github.com/ChaoqunZheng/ESMH.

Multisource Data Reconstruction-Based Deep Unsupervised Hashing for Unisource Remote Sensing Image Retrieval

Unsupervised hashing for remote sensing (RS) image retrieval first extracts image features and then use these features to construct supervised information (e.g., pseudo-labels) to train hashing networks. Existing methods usually regard RS images as natural images to extract unisource features. However, these features only contain partial information about ground objects and cannot produce reliable pseudo-labels. In addition, existing methods only generate a pseudo single-label to annotate each RS image, which cannot accurately represent multiple scenes in a RS image. To address these drawbacks, this paper proposes a new Multisource data reconstruction-based deep unsupervised Hashing method, called MrHash, which explores the characteristics of RS images to construct reliable pseudo-labels. In particular, we first use geographic coordinates to obtain different satellite images and develop a novel autoencoder network to extract multisource features from these images. Then pseudo multi-labels are designed to deal with the coexistence of multiple scenes in a single image. These labels are generated by a custom probability function with extracted multisource features. Finally, we propose a novel multi-semantic hash loss by using the Kull-back–Leibler (KL) divergence to preserve the semantic similarity of these pseudo multi-labels in Hamming space. Our newly developed MrHash only uses multisource images to construct supervised information, and hash code generation still relies on a unisource input image. Experiments on benchmark datasets clearly show the superiority of the proposed method over state-of-the-art baselines. https://github.com/sunyuxi/MrHash.

Unsupervised deep hashing through learning soft pseudo label for remote sensing image retrieval

Unsupervised hashing is an important approach for large-scale content-based remote sensing (RS) image retrieval. Existing unsupervised hashing methods usually utilize data clustering to generate pseudo labels as supervised signals. In the RS domain, images of each class cannot be accurately grouped into individual clusters because image features are not well characterized by the pre-trained models learned on a nature image dataset. As a result, to preserve the similarity of images sharing at least one class, intra-cluster and inter-cluster similarities need to be carefully learned. However, existing pseudo-labels are hard pseudo-labels represented by scalar values, which cannot well reflect the semantic distance between inter-cluster images or the semantic distance between intra-cluster images. To address these problems, this paper proposes a soft-pseudo-label-based unsupervised deep hashing method for content-based RS image retrieval, called SPL-UDH. Soft pseudo-labels can accurately describe the global similarity between inter-cluster images by binarized vectors. Specifically, we design a deep auto-encoder network to learn soft pseudo-labels automatically and meanwhile to generate a local similarity matrix representing the proximity between intra-cluster images. Based on soft pseudo-labels and local similarity matrix, we propose a deep hashing network to simultaneously learn the inter-cluster similarity and the intra-cluster similarity between RS images. Moreover, we design a new objective function based on Bayesian theory so that the deep hashing network can be trained by jointly learning the soft pseudo-labels and the local similarity matrix. Extensive experiments on public RS image retrieval datasets demonstrate that SPL-UDH outperforms various state-of-the-art unsupervised hashing methods.

Aggregation-Based Graph Convolutional Hashing for Unsupervised Cross-Modal Retrieval

Cross-modal hashing has sparked much attention in large-scale information retrieval for its storage and query efficiency. Despite the great success achieved by supervised approaches, existing unsupervised hashing methods still suffer from the lack of reliable learning guidance and cross-modal discrepancy. In this paper, we propose Aggregation-based Graph Convolutional Hashing (AGCH) to tackle these obstacles. First, considering that a single similarity metric can hardly represent data relationships comprehensively, we develop an efficient aggregation strategy that utilises multiple metrics to construct a more precise affinity matrix for learning. Specifically, we apply various similarity measures to exploit the structural information of multiple modalities from different perspectives and then aggregate the obtained information to produce a joint similarity matrix. Furthermore, a novel deep model is designed to learn unified binary codes across different modalities, where the key components include modality-specific encoders, Graph Convolutional Networks (GCNs) and a fusion module. The modality-specific encoders are tasked to learn feature embeddings for each individual modality. On this basis, we leverage GCNs to further excavate the semantic structure of data, along with a fusion module to correlate different modalities. Extensive experiments on three real-world datasets demonstrate that the proposed method significantly outperforms the state-of-the-art competitors.

Unsupervised Discriminative Deep Hashing With Locality and Globality Preservation

Deep hashing has greatly improved retrieval performance with the powerful learning capability of deep neural network. However, deep unsupervised hashing can hardly achieve impressive performance due to the lack of the semantic supervision. This letter proposes Unsupervised Discriminative Deep Hashing (UD $^2$ H) to fulfill this gap. UD $^2$ H is formulated to jointly perform hash code learning and clustering, and trained in an asymmetric manner to improve the efficiency. The cluster labels supervise the training of deep model to enable hash code discriminative. Based on the outputs of the deep model, UD $^2$ H adaptively constructs a similarity graph that considers the local and global structures. Experiments on three benchmark datasets show that the proposed UD $^2$ H outperforms the state-of-the-art unsupervised deep hashing methods.

Local Semantic Structure Captured and Instance Discriminated by Unsupervised Hashing

Recently, unsupervised Hashing has attracted much attention in the machine learning and information retrieval communities, due to its low storage and high search efficiency. Most of existing unsupervised Hashing methods rely on the local semantic structure of the data as the guiding information, requiring to preserve such semantic structure in the Hamming space. Thus, how to precisely represent the local structure of the data and Hashing code s becomes the key point to success. This study proposes a novel Hashing method based on self-supervised learning. Specifically, it is proposed to utilize the contrast learning to acquire a compact and accurate feature representation for each sample, and then a semantic structure matrix can be constructed for representing the similarity between samples. Meanwhile, a new loss function is proposed to preserve the semantic information and improve the discriminative ability in the Hamming space, by the spirit of the instance discrimination method proposed recently. The proposed framework is end-to-end trainable. Extensive experiments on two large-scale image retrieval data sets show that the proposed method can significantly outperform current state-of-the-art methods.

Self-Collaborative Unsupervised Hashing for Large-Scale Image Retrieval

Learning based hashing approaches have achieved considerable success in large-scale image retrieval due to the query effectiveness and efficiency. However, most studies highly rely on supervised knowledge like data labels, thus might fail in unsupervised setting. To address this issue, we propose a self-collaborative unsupervised hashing method (SCUH), which jointly learns hashing function and virtual labels in a self-collaborative manner. Different from existing unsupervised counterparts, SCUH treats sample features and the learned virtual labels as two groups of different features in a fair view. This results in two groups of transformation matrices, which have proven beneficial for capturing diversity information of different features to better advance binary codes. Moreover, we devise an alternate optimization algorithm to solve SCUH, in which each sub-problem has a closed-form solution as a byproduct. Experiments of image retrieval on three datasets show that SCUH performs favorably over eight comparative methods including unsupervised counterparts.

Sparse graph based self-supervised hashing for scalable image retrieval

In recent years, learning-based image hashing techniques have elicited wide interest among researchers because they can be applied in high-dimensional data such as videos and images. Supervised hashing techniques can achieve a satisfactory retrieval performance, but are excessively reliant on label information, resulting in the appearance of unsupervised hashing methods. However, existing unsupervised hashing methods often fail to obtain effective similarity information from the training set; this causes a large degradation in their retrieval performance. Some pseudo label-based methods partially alleviate this problem, but are sensitive to the pre-defined number of categories. Therefore, in this paper, we mainly discuss a solution to the above-mentioned problems regarding image hashing. We propose a sparse graph based self-supervised hashing method in which a sparse graph is constructed, which not only circumvents the requirement of a predefined number of categories as compared with pseudo label-based methods, but also significantly reduces the memory demand compared with a dense graph-based method. In addition, we also exploit a self-supervised reconstruction constraint to further preserve the semantic information. These items are combined in a linear manner and optimized using an iterative strategy. Four representative datasets, including two single-label and two multi-label datasets, are employed to evaluate our method. The results, based on multiple metrics, show that our method can outperform other state-of-the-art methods.

Unsupervised Hashing with Gradient Attention

The existing learning-based unsupervised hashing method usually uses a pre-trained network to extract features, and then uses the extracted feature vectors to construct a similarity matrix which guides the generation of hash codes through gradient descent. Existing research shows that the algorithm based on gradient descent will cause the hash codes of the paired images to be updated toward each other’s position during the training process. For unsupervised training, this situation will cause large fluctuations in the hash code during training and limit the learning efficiency of the hash code. In this paper, we propose a method named Deep Unsupervised Hashing with Gradient Attention (UHGA) to solve this problem. UHGA mainly includes the following contents: (1) use pre-trained network models to extract image features; (2) calculate the cosine distance of the corresponding features of the pair of images, and construct a similarity matrix through the cosine distance to guide the generation of hash codes; (3) a gradient attention mechanism is added during the training of the hash code to pay attention to the gradient. Experiments on two existing public datasets show that our proposed method can obtain more discriminating hash codes.