Asymmetric Deep Hashing for Efficient Hash Code Compression

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.

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Triplet Deep Hashing with Joint Supervised Loss Based on Deep Neural Networks

Computational Intelligence and Neuroscience ◽

10.1155/2019/8490364 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Mingyong Li ◽

Ziye An ◽

Qinmin Wei ◽

Kaiyue Xiang ◽

Yan Ma

Keyword(s):

Neural Network ◽

Deep Learning ◽

Large Scale ◽

Selection Method ◽

Image Features ◽

Multimedia Data ◽

Image Feature ◽

Great Success ◽

Deep Hashing ◽

Hash Code

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.

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Deep Supervised Hashing for Fast Multi-Label Image

MATEC Web of Conferences ◽

10.1051/matecconf/201817303032 ◽

2018 ◽

Vol 173 ◽

pp. 03032

Author(s):

Qian Ying ◽

Ye Qingqing

Keyword(s):

Objective Function ◽

Loss Function ◽

Binary Code ◽

Hamming Distance ◽

Binary Vector ◽

Experimental Results ◽

Training Data ◽

Retrieval Accuracy ◽

Deep Hashing ◽

Hash Code

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.

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Semi-Supervised Deep Hashing with a Bipartite Graph

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/452 ◽

2017 ◽

Cited By ~ 12

Author(s):

Xinyu Yan ◽

Lijun Zhang ◽

Wu-Jun Li

Keyword(s):

Neural Network ◽

Deep Learning ◽

Bipartite Graph ◽

Deep Neural Network ◽

State Of The Art ◽

Underlying Structure ◽

Deep Hashing ◽

Hash Code ◽

Out Of Sample ◽

Hash Codes

Recently, deep learning has been successfully applied to the problem of hashing, yielding remarkable performance compared to traditional methods with hand-crafted features. However, most of existing deep hashing methods are designed for the supervised scenario and require a large number of labeled data. In this paper, we propose a novel semi-supervised hashing method for image retrieval, named Deep Hashing with a Bipartite Graph (DHBG), to simultaneously learn embeddings, features and hash codes. More specifically, we construct a bipartite graph to discover the underlying structure of data, based on which an embedding is generated for each instance. Then, we feed raw pixels as well as embeddings to a deep neural network, and concatenate the resulting features to determine the hash code. Compared to existing methods, DHBG is a universal framework that is able to utilize various types of graphs and losses. Furthermore, we propose an inductive variant of DHBG to support out-of-sample extensions. Experimental results on real datasets show that our DHBG outperforms state-of-the-art hashing methods.

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Deep Binary Representation for Efficient Image Retrieval

Advances in Multimedia ◽

10.1155/2017/8961091 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Xuchao Lu ◽

Li Song ◽

Rong Xie ◽

Xiaokang Yang ◽

Wenjun Zhang

Keyword(s):

Image Retrieval ◽

Hamming Distance ◽

Binary Representation ◽

Deep Network ◽

Deep Hashing ◽

Hash Code ◽

Representation Method ◽

The Relationship ◽

Determining Factor ◽

Hash Codes

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.

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