Semi-Supervised Deep Hashing with a Bipartite Graph

Hashing has drawn more and more attention in image retrieval due to its high search speed and low storage cost. Traditional hashing methods project the high-dimensional hand-crafted visual features to compact binary codes by linear or non-linear hashing functions. Deep hashing methods, which integrate image representation learning and hash functions learning into a unified framework, have shown more superior performance. Most of existing supervised deep hashing methods mainly consider the semantic similarities among images by using pair-wise or triplet-wise constraints as supervision information. However, as a kind of crucial information, the rankings of the retrieval results, are neglected. Consequently, the produced hash codes may be suboptimal. In this paper, a new Deep Hashing with Top Similarity Preserving (DHTSP) method is proposed to optimize the quality of hash codes for image retrieval. Specifically, we utilize AlexNet to extract discriminative image representations directly from the raw image pixels and learn hash functions simultaneously. Then a top similarity preserving loss function is designed to preserve the similarity of returned images at the top of the ranking list. Experimental results on three benchmark datasets show that our proposed method outperforms most of state-of-the-art deep hashing methods and traditional hashing methods.

Hashing method maps similar data to binary hashcodes with smaller hamming distance, which has received broad attention due to its low storage cost and fast retrieval speed. With the rapid development of deep learning, deep hashing methods have achieved promising results in efficient information retrieval. Most existing deep hashing methods adopt pairwise or triplet losses to deal with similarities underlying the data, but their training are difficult and less efficient because O(n2) data pairs and O(n3) triplets are involved. To address these issues, we propose a novel deep hashing algorithm with unary loss which can be trained very efficiently. First of all, we introduce a Unary Upper Bound of the traditional triplet loss, thus reducing the complexity to O(n) and bridging the classificationbased unary loss and the triplet loss. Second, we propose a novel Semantic Cluster Deep Hashing (SCDH) algorithm by introducing a modified Unary Upper Bound loss, named Semantic Cluster Unary Loss (SCUL). The resultant hashcodes form several compact clusters, which means hashcodes in the same cluster have similar semantic information. We also demonstrate that the proposed SCDH is easy to be extended to semi-supervised settings by incorporating the state-of-the-art semi-supervised learning algorithms. Experiments on large-scale datasets show that the proposed method is superior to state-of-the-art hashing algorithms.

Deep hashing methods utilize an end-to-end framework to mutually learn feature representations and hash codes, thereby achieving a better retrieval performance. Traditional supervised hashing methods adopt handcrafted features for hashing function learning and then generate hash codes through classification and quantization. The lack of adaptability and independence of the quantization procedure leads to low retrieval accuracy of supervised hashing methods with handcrafted features in image retrieval. In this study, a non-relaxation deep hashing method for fast image retrieval is proposed. In this method, a differentiable host thresholding function is used to encourage hash-like codes to approach -1 or 1 non-linearly at the output of the convolutional neural, instead of the symbol function for quantization used in the traditional method. The output of the host thresholding function is directly used to compute the network training error, and a loss function is elaborately designed with the norm to constrain each bit of the hash-like code to be as binary as possible. Finally, a symbol function is added outside the trained network model to generate binary hash codes for image storage and retrieval in a low-dimensional binary space. Extensive experiments on two large-scale public datasets show that our method can effectively learn image features, generate accurate binary hash codes, and outperform state-of-the-art methods in terms of the mean average precision.

In recent years, with the explosion of multimedia data from search engines, social media, and e-commerce platforms, there is an urgent need for fast retrieval methods for massive big data. Hashing is widely used in large-scale and high-dimensional data search because of its low storage cost and fast query speed. Thanks to the great success of deep learning in many fields, the deep learning method has been introduced into hashing retrieval, and it uses a deep neural network to learn image features and hash codes simultaneously. Compared with the traditional hashing methods, it has better performance. However, existing deep hashing methods have some limitations; for example, most methods consider only one kind of supervised loss, which leads to insufficient utilization of supervised information. To address this issue, we proposed a triplet deep hashing method with joint supervised loss based on the convolutional neural network (JLTDH) in this work. The proposed method JLTDH combines triplet likelihood loss and linear classification loss; moreover, the triplet supervised label is adopted, which contains richer supervised information than that of the pointwise and pairwise labels. At the same time, in order to overcome the cubic increase in the number of triplets and make triplet training more effective, we adopt a novel triplet selection method. The whole process is divided into two stages: In the first stage, taking the triplets generated by the triplet selection method as the input of the CNN, the three CNNs with shared weights are used for image feature learning, and the last layer of the network outputs a preliminary hash code. In the second stage, relying on the hash code of the first stage and the joint loss function, the neural network model is further optimized so that the generated hash code has higher query precision. We perform extensive experiments on the three public benchmark datasets CIFAR-10, NUS-WIDE, and MS-COCO. Experimental results demonstrate that the proposed method outperforms the compared methods, and the method is also superior to all previous deep hashing methods based on the triplet label.

Deep debiased contrastive hashing

Deep supervised hashing hash has been widely utilized in large-scale image retrieval due to its lightweight storage and fast search speed. The distribution of features from the existing hashing methods has been inevitably distorted from the original feature distribution, which resulted in the performance decline on image retrieval. With the constraints of the Euclidian metric, the distortion can be mitigated to certain extent. However, the Euclidian distance is sensitive to the norm of the features. On the point, we propose a novel deep hashing method, called cosine metric supervised deep hashing (CMDH), to perform hash learning by incorporating cosine metric and category loss. CMDH uses the cosine metric to effectively mitigate the impact caused by the diverse sample vector norms on retrieval performance. In addition, joint deep hash learning constructs binary coding over cosine loss and category loss, which further alleviates distortion in binary feature learning for efficient retrieval.KeywordsCosine metricClassification informationSupervised deep hashingImage retrieval

Multiple-Instance ranking based deep hashing for multi-Label image retrieval

Recently, clinical phenotypic semantic information has begun to play an important role in some brain network classification methods based on deep learning. However, most current methods only consider the phenotypic semantic information of individual brain networks but ignore the potential phenotypic characteristics among group brain networks. To address this problem, we present a deep hashing mutual learning (DHML)-based brain network classification method. Specifically, we first design a separable CNN-based deep hashing learning to extract individual topological features of brain networks and map them into hash codes. Secondly, we construct a group brain network relationship graph based on the similarity of phenotypic semantic information, in which each node is a brain network, and the properties of the nodes are the individual features extracted in the previous step. Then, we adopt a GCN-based deep hashing learning to extract the group topological features of the brain network and map them to hash codes. Finally, the two deep hashing learning models perform mutual learning by measuring the distribution differences between the hash codes to achieve the interaction of individual and group features. The experimental results on the three commonly used brain atlases (AAL Atlas, Dosenbach160 Atlas, and CC200 Atlas) of the ABIDE I dataset show that our proposed DHML method achieves optimal classification performance compared with some state-of-the-art methods.

Deep hashing methods have achieved promising results for large-scale image retrieval recently. To accelerate the subsequent Hamming ranking process, the multi-index approach has been proposed to reduce the computations for the Hamming distance. However, the binary codes output by the previous deep hashing methods may not be optimally compatible with the multi-index approach. In this paper, we present a novel Deep Index-Compatible Hashing (DICH) method for fast image retrieval, which can learn similarity-preserving binary codes that are more compatible with the multi-index approach. With the learned binary codes, both the size of the intermediate result set produced by the multi-index approach and the number of the candidate images can be reduced, which can accelerate the Hamming ranking process. By taking advantage of the unique feature of DICH, we further propose a block-based ranking strategy to quickly rank the candidate images without calculating the Hamming distance. Extensive evaluations demonstrate that the proposed method can significantly reduce the retrieval time with almost no loss of retrieval accuracy.

Similarity preserving hashing methods have drawn a lot of attention lately due to their efficiency in performing approximate nearest neighbor searches on high-dimensional large-scale multimedia data. With the development of deep learning techniques, deep supervised hashing has attracted increasing attention lately. Existing deep hashing methods usually take a two-step strategy to generate hash codes. They generate continuous hash codes and then discretize them into binary hash codes. Deep hashing methods have outperformed other methods significantly, but there is still a huge quantization gap between the discrete hash codes and continuous hash codes. The quantization gap prevents existing deep hashing methods from achieving better performance. In this paper, we propose deep supervised hashing with dynamic weighting scheme (DHDW), a novel deep hashing method that narrows down the quantization gap by utilizing Hamming distance information during training. Specifically, we introduce Hamming distance and stability information into the training phase by applying a novel dynamic weighting module to all data of a mini-batch. The dynamic weighting module we present is constructed from real-time Hamming distance and stability information. Additionally, the dynamic weighting module is compatible with all existing pairwise deep supervised hashing methods. Comprehensive experiments show that DHDW outperforms existing state-of-the-art methods on several standard datasets and benchmarks, and the dynamic weighting module effectively improves the performance of deep learning to hash methods.

Discriminative deep metric learning for asymmetric discrete hashing

An efficient dual semantic preserving hashing for cross-modal retrieval

The application of deep hashing methods for cross-modal retrieval has seen growing interest due to their storage efficiency and fast query execution. However, the challenge posed by the “heterogeneity gap” in multi-modal datasets cannot be understated. To address this, we present a novel framework named Dual-Pathway Deep Hashing-Based Adversarial Learning (DP-DHAL), engineered to surmount this challenge. The architecture of DP-DHAL integrates three key components: (a) a dual-pathway representation learning module tasked with extracting modality-specific features; (b) an adversarial module working to align the distributions of cross-modal features; and (c) a deep hashing module responsible for generating hash codes that uphold the similarity relationships across different modalities. Additionally, we have developed a unique Hamming triplet-margin loss function to refine the assessment of content similarities. The DP-DHAL model is trained through an adversarial process where the adversarial module’s goal is to discern cross-modal features with the aim of reducing the heterogeneity gap. Simultaneously, the representation learning module is focused on producing representations that can both deceive the adversarial module and preserve cross-modal similarities to yield distinctive hash codes. Comprehensive experiments on varied datasets have shown that our proposed method outperforms other leading cross-modal hashing techniques.

Hashing could significantly accelerate large-scale image search by transforming the high-dimensional features into binary Hamming space, where efficient similarity search can be achieved with very fast Hamming distance computation and extremely low storage cost. As an important branch of hashing methods, multi-view hashing takes advantages of multiple features from different views for binary hash learning. However, existing multi-view hashing methods are either based on shallow models which fail to fully capture the intrinsic correlations of heterogeneous views, or unsupervised deep models which suffer from insufficient semantics and cannot effectively exploit the complementarity of view features. In this paper, we propose a novel Deep Collaborative Multi-view Hashing (DCMVH) method to deeply fuse multi-view features and learn multi-view hash codes collaboratively under a deep architecture. DCMVH is a new deep multi-view hash learning framework. It mainly consists of 1) multiple view-specific networks to extract hidden representations of different views, and 2) a fusion network to learn multi-view fused hash code. DCMVH associates different layers with instance-wise and pair-wise semantic labels respectively. In this way, the discriminative capability of representation layers can be progressively enhanced and meanwhile the complementarity of different view features can be exploited effectively. Finally, we develop a fast discrete hash optimization method based on augmented Lagrangian multiplier to efficiently solve the binary hash codes. Experiments on public multi-view image search datasets demonstrate our approach achieves substantial performance improvement over state-of-the-art methods.

In order to improve the retrieval efficiency and accuracy of the existing encrypted speech retrieval methods, and improve the semantic representation of speech features and classification performance, a classification retrieval method for encrypted speech based on deep neural network (DNN) and deep hashing is proposed. Firstly, the speech files are classified according to the category tags, and the speech files are encrypted by Rossler chaotic map method and uploaded to the cloud encrypted speech library. Secondly, the Log-Mel spectrogram features of the original speech are extracted, and extract deep semantic features and generate classification results through the trained convolutional neural network (CNN) and convolutional recurrent neural network (CRNN). Finally, the semantic feature hash code is obtained through the constructed hash function, combined with the category hash code encoded by One Hot coding to obtain the final deep hashing binary code, and uploaded to the deep hashing index table. When retrieval, the deep hashing binary code of the query speech is obtained, and the “two-stage” classification retrieval strategy and the normalized Hamming distance algorithm are used to match the semantic feature hash. Experimental results show that the proposed two DNN coding models have excellent feature learning performance, and has better recall rate, precision rate and retrieval efficiency.