Deep Hashing Methods Research Articles

A novel deep hashing method for fast image retrieval

In recent years, the deep hashing image retrieval algorithm has become a hot spot in current research. Although the deep hashing algorithm has achieved good results in image retrieval, how to further improve the accuracy of the image retrieval algorithm and reduce the computational complexity of the algorithm, the two basic requirements of the algorithm, need attention in image retrieval. The paper proposes a new Aggregate Deep Fast Supervised Discrete Hashing (ADFSDH) method for highly efficient image retrieval on large-scale datasets. Specifically, in order to improve the algorithm performance, the paper first proposes a new Aggregate Deep Convolutional Neural Network (ADCNN) mode based on VGG16, VGG19 and transfer learning for effective image feature extraction, which contains two different feature extractors in parallel. And then, the paper proposes a new feature fusion method. When our weighted proportion is consistent with the Mean Average Precision results of two different feature extractors, we can obtain the most accurate description of the image. Firstly, in order to save ADCNN required storage space and improve ADCNN image retrieval efficiency, the Fast Supervised Discrete Hashing algorithm after adjusting the parameters is introduced into the ADCNN model. In addition, ADFSDH unifies feature learning and hash coding into the same framework. The proposed method was experimented on three datasets (CIFAR10, MNIST and FD-XJ), and the result shows that it is superior to the current mainstream approaches in image retrieval.

Deep Discrete Supervised Hashing.

Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised hashing. Recently, discrete supervised hashing and feature learning based deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, utilizing supervised information to directly guide discrete (binary) coding procedure can avoid sub-optimal solution and improve the accuracy. On the other hand, feature learning based deep hashing, which integrates deep feature learning and hash-code learning into an end-to-end architecture, can enhance the feedback between feature learning and hash-code learning. The key in discrete supervised hashing is to adopt supervised information to directly guide the discrete coding procedure in hashing. The key in deep hashing is to adopt the supervised information to directly guide the deep feature learning procedure. However, most deep supervised hashing methods cannot use the supervised information to directly guide both discrete (binary) coding procedure and deep feature learning procedure in the same framework. In this paper, we propose a novel deep hashing method, called deep discrete supervised hashing (DDSH). DDSH is the first deep hashing method which can utilize pairwise supervised information to directly guide both discrete coding procedure and deep feature learning procedure and thus enhance the feedback between these two important procedures. Experiments on four real datasets show that DDSH can outperform other state-of-the-art baselines, including both discrete hashing and deep hashing baselines, for image retrieval.

Fused feature encoding in convolutional neural network

Recently, deep hashing (DH) methods have been proposed to learn specific image representations and a series of hash functions. However, existing DH methods mainly use convolutional neural networks (CNN) to extract global features, losing some local information. What’s more, the pairwise or triplet wise model applied in DH methods increases computational complexity and storage requirements. In this paper, we propose a new DH method called fused feature encoding (FFE). In FFE, we introduce a bypass from the intermediate convolutional layer to extract images’ local information and unify local and global information into one neural network to explore richer semantic information within the image. In our model, the number of neurons in the global or local encoding layer corresponds to the number of global or local encoding bits respectively. We also apply a new method to update the weights in our network to improve the efficiency. Experimental results show the superiority of the proposed approach over the state-of-the-arts.

Semantic Cluster Unary Loss for Efficient Deep Hashing.

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.

Supervised Deep Hashing for Hierarchical Labeled Data

Recently, hashing methods have been widely used in large-scale image retrieval. However, most existing supervised hashing methods do not consider the hierarchical relation of labels,which means that they ignored the rich semantic information stored in the hierarchy. Moreover, most of previous works treat each bit in a hash code equally, which does not meet the scenario of hierarchical labeled data. To tackle the aforementioned problems, in this paper, we propose a novel deep hashing method, called supervised hierarchical deep hashing (SHDH), to perform hash code learning for hierarchical labeled data. Specifically, we define a novel similarity formula for hierarchical labeled data by weighting each level, and design a deep neural network to obtain a hash code for each data point. Extensive experiments on two real-world public datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task.

Dual Deep Neural Networks Cross-Modal Hashing

Recently, deep hashing methods have attracted much attention in multimedia retrieval task. Some of them can even perform cross-modal retrieval. However, almost all existing deep cross-modal hashing methods are pairwise optimizing methods, which means that they become time-consuming if they are extended to large scale datasets. In this paper, we propose a novel tri-stage deep cross-modal hashing method – Dual Deep Neural Networks Cross-Modal Hashing, i.e., DDCMH, which employs two deep networks to generate hash codes for different modalities. Specifically, in Stage 1, it leverages a single-modal hashing method to generate the initial binary codes of textual modality of training samples; in Stage 2, these binary codes are treated as supervised information to train an image network, which maps visual modality to a binary representation; in Stage 3, the visual modality codes are reconstructed according to a reconstruction procedure, and used as supervised information to train a text network, which generates the binary codes for textual modality. By doing this, DDCMH can make full use of inter-modal information to obtain high quality binary codes, and avoid the problem of pairwise optimization by optimizing different modalities independently. The proposed method can be treated as a framework which can extend any single-modal hashing method to perform cross-modal search task. DDCMH is tested on several benchmark datasets. The results demonstrate that it outperforms both deep and shallow state-of-the-art hashing methods.

Deep hashing with top similarity preserving for image retrieval

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.

Deep Supervised Hashing for Fast Multi-Label Image

In this paper, most of the existing Hashing methods is mapping the hand extracted features to binary code, and designing the loss function with the label of images. However, hand-crafted features and inadequacy considering all the loss of the network will reduce the retrieval accuracy. Supervised hashing method improves the similarity between sample and hash code by training data and labels of image. In this paper, we propose a novel deep hashing method which combines the objective function with pairwise label which is produced by the Hamming distance between the label binary vector of images, quantization error and the loss of hashing code between the balanced value as loss function to train network. The experimental results show that the proposed method is more accurate than most of current restoration methods.

SSDH: Semi-Supervised Deep Hashing for Large Scale Image Retrieval

Hashing methods have been widely used for efficient similarity retrieval on large scale image database. Traditional hashing methods learn hash functions to generate binary codes from hand-crafted features, which achieve limited accuracy since the hand-crafted features cannot optimally represent the image content and preserve the semantic similarity. Recently, several deep hashing methods have shown better performance because the deep architectures generate more discriminative feature representations. However, these deep hashing methods are mainly designed for supervised scenarios, which only exploit the semantic similarity information, but ignore the underlying data structures. In this paper, we propose the semi-supervised deep hashing (SSDH) approach, to perform more effective hash function learning by simultaneously preserving semantic similarity and underlying data structures. The main contributions are as follows: (1) We propose a semi-supervised loss to jointly minimize the empirical error on labeled data, as well as the embedding error on both labeled and unlabeled data, which can preserve the semantic similarity and capture the meaningful neighbors on the underlying data structures for effective hashing. (2) A semi-supervised deep hashing network is designed to extensively exploit both labeled and unlabeled data, in which we propose an online graph construction method to benefit from the evolving deep features during training to better capture semantic neighbors. To the best of our knowledge, the proposed deep network is the first deep hashing method that can perform hash code learning and feature learning simultaneously in a semi-supervised fashion. Experimental results on 5 widely-used datasets show that our proposed approach outperforms the state-of-the-art hashing methods.

Hierarchical deep hashing for image retrieval

We present a new method to generate efficient multi-level hashing codes for image retrieval based on the deep siamese convolutional neural network (DSCNN). Conventional deep hashing methods trade off the capability of capturing highly complex and nonlinear semantic information of images against very compact hash codes, usually leading to high retrieval efficiency but with deteriorated accuracy. We alleviate the restrictive compactness requirement of hash codes by extending them to a two-level hierarchical coding scheme, in which the first level aims to capture the high-level semantic information extracted by the deep network using a rich encoding strategy, while the subsequent level squeezes them to more global and compact codes. At running time, we adopt an attention-based mechanism to select some of its most essential bits specific to each query image for retrieval instead of using the full hash codes of the first level. The attention-based mechanism is based on the guides of hash codes generated by the second level, taking advantage of both local and global properties of deep features. Experimental results on various popular datasets demonstrate the advantages of the proposed method compared to several state-of-the-art methods.

Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval

Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN) search for the large-scale image retrieval to solve the problem of storage and retrieval efficiency. Recently, deep hashing methods have been proposed to perform the simultaneous feature learning and the hash code learning with deep neural networks. Even though deep hashing has shown the better performance than traditional hashing methods with handcrafted features, the learned compact hash code from one deep hashing network may not provide the full representation of an image. In this paper, we propose a novel hashing indexing method, called the Deep Hashing based Fusing Index (DHFI), to generate a more compact hash code which has stronger expression ability and distinction capability. In our method, we train two different architecture’s deep hashing subnetworks and fuse the hash codes generated by the two subnetworks together to unify images. Experiments on two real datasets show that our method can outperform state-of-the-art image retrieval applications.

Deep Binary Representation for Efficient Image Retrieval

With the fast growing number of images uploaded every day, efficient content-based image retrieval becomes important. Hashing method, which means representing images in binary codes and using Hamming distance to judge similarity, is widely accepted for its advantage in storage and searching speed. A good binary representation method for images is the determining factor of image retrieval. In this paper, we propose a new deep hashing method for efficient image retrieval. We propose an algorithm to calculate the target hash code which indicates the relationship between images of different contents. Then the target hash code is fed to the deep network for training. Two variants of deep network, DBR and DBR-v3, are proposed for different size and scale of image database. After training, our deep network can produce hash codes with large Hamming distance for images of different contents. Experiments on standard image retrieval benchmarks show that our method outperforms other state-of-the-art methods including unsupervised, supervised, and deep hashing methods.