scholarly journals Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval

2021 ◽  
Vol 11 (18) ◽  
pp. 8769
Author(s):  
Jun Long ◽  
Longzhi Sun ◽  
Liujie Hua ◽  
Zhan Yang
Keyword(s):  
Code Generation ◽  
Hash Function ◽  
Large Scale ◽  
Semantic Information ◽  
Multimedia Data ◽  
Multimedia Retrieval ◽  
Coordinate Descent Algorithm ◽  
Hash Code ◽  
Benchmark Datasets ◽  
Hash Codes

Cross-modal hashing technology is a key technology for real-time retrieval of large-scale multimedia data in real-world applications. Although the existing cross-modal hashing methods have achieved impressive accomplishment, there are still some limitations: (1) some cross-modal hashing methods do not make full consider the rich semantic information and noise information in labels, resulting in a large semantic gap, and (2) some cross-modal hashing methods adopt the relaxation-based or discrete cyclic coordinate descent algorithm to solve the discrete constraint problem, resulting in a large quantization error or time consumption. Therefore, in order to solve these limitations, in this paper, we propose a novel method, named Discrete Semantics-Guided Asymmetric Hashing (DSAH). Specifically, our proposed DSAH leverages both label information and similarity matrix to enhance the semantic information of the learned hash codes, and the ℓ2,1 norm is used to increase the sparsity of matrix to solve the problem of the inevitable noise and subjective factors in labels. Meanwhile, an asymmetric hash learning scheme is proposed to efficiently perform hash learning. In addition, a discrete optimization algorithm is proposed to fast solve the hash code directly and discretely. During the optimization process, the hash code learning and the hash function learning interact, i.e., the learned hash codes can guide the learning process of the hash function and the hash function can also guide the hash code generation simultaneously. Extensive experiments performed on two benchmark datasets highlight the superiority of DSAH over several state-of-the-art methods.

scholarly journals TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data

2021 ◽  
Vol 11 (22) ◽  
pp. 10803
Author(s):  
Jiagang Song ◽  
Yunwu Lin ◽  
Jiayu Song ◽  
Weiren Yu ◽  
Leyuan Zhang
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Location Based Services ◽  
Multimedia Data ◽  
Multimedia Retrieval ◽  
Geographical Information ◽  
Superior Performance ◽  
Hybrid Index ◽  
High Level ◽  
Hash Codes

Mass multimedia data with geographical information (geo-multimedia) are collected and stored on the Internet due to the wide application of location-based services (LBS). How to find the high-level semantic relationship between geo-multimedia data and construct efficient index is crucial for large-scale geo-multimedia retrieval. To combat this challenge, the paper proposes a deep cross-modal hashing framework for geo-multimedia retrieval, termed as Triplet-based Deep Cross-Modal Retrieval (TDCMR), which utilizes deep neural network and an enhanced triplet constraint to capture high-level semantics. Besides, a novel hybrid index, called TH-Quadtree, is developed by combining cross-modal binary hash codes and quadtree to support high-performance search. Extensive experiments are conducted on three common used benchmarks, and the results show the superior performance of the proposed method.

scholarly journals Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval

2019 ◽  
Vol 33 ◽  
pp. 176-183 ◽  
Author(s):  
Chao Li ◽  
Cheng Deng ◽  
Lei Wang ◽  
De Xie ◽  
Xianglong Liu
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Storage Cost ◽  
Common Representation ◽  
Benchmark Datasets ◽  
Query Efficiency ◽  
Hash Codes

In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval. Benefiting from deep leaning, continuously compelling results in cross-modal retrieval community have been achieved. However, existing deep cross-modal hashing methods either rely on amounts of labeled information or have no ability to learn an accuracy correlation between different modalities. In this paper, we proposed Unsupervised coupled Cycle generative adversarial Hashing networks (UCH), for cross-modal retrieval, where outer-cycle network is used to learn powerful common representation, and inner-cycle network is explained to generate reliable hash codes. Specifically, our proposed UCH seamlessly couples these two networks with generative adversarial mechanism, which can be optimized simultaneously to learn representation and hash codes. Extensive experiments on three popular benchmark datasets show that the proposed UCH outperforms the state-of-the-art unsupervised cross-modal hashing methods.

scholarly journals Large-Scale Person Re-Identification Based on Deep Hash Learning

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 449 ◽  
Author(s):  
Xian-Qin Ma ◽  
Chong-Chong Yu ◽  
Xiu-Xin Chen ◽  
Lan Zhou
Keyword(s):  
Hash Function ◽  
Large Scale ◽  
Research Topic ◽  
Cross Entropy ◽  
Network Layers ◽  
Hash Code ◽  
Image Hash ◽  
Challenging Research ◽  
Fully Connected ◽  
Relationship Of

Person re-identification in the image processing domain has been a challenging research topic due to the influence of pedestrian posture, background, lighting, and other factors. In this paper, the method of harsh learning is applied in person re-identification, and we propose a person re-identification method based on deep hash learning. By improving the conventional method, the method proposed in this paper uses an easy-to-optimize shallow convolutional neural network to learn the inherent implicit relationship of the image and then extracts the deep features of the image. Then, a hash layer with three-step calculation is incorporated in the fully connected layer of the network. The hash function is learned and mapped into a hash code through the connection between the network layers. The generation of the hash code satisfies the requirements that minimize the error of the sum of quantization loss and Softmax regression cross-entropy loss, which achieve the end-to-end generation of hash code in the network. After obtaining the hash code through the network, the distance between the pedestrian image hash code to be retrieved and the pedestrian image hash code library is calculated to implement the person re-identification. Experiments conducted on multiple standard datasets show that our deep hashing network achieves the comparable performances and outperforms other hashing methods with large margins on Rank-1 and mAP value identification rates in pedestrian re-identification. Besides, our method is predominant in the efficiency of training and retrieval in contrast to other pedestrian re-identification algorithms.

scholarly journals Deep Joint Semantic-Embedding Hashing

2018 ◽  
Author(s):  
Ning Li ◽  
Chao Li ◽  
Cheng Deng ◽  
Xianglong Liu ◽  
Xinbo Gao
Keyword(s):  
Large Scale ◽  
Semantic Level ◽  
Semantic Correlation ◽  
Semantic Embedding ◽  
Proposed Model ◽  
Label Information ◽  
Benchmark Datasets ◽  
Large Scale Image Retrieval ◽  
Almost All ◽  
Hash Codes

Hashing has been widely deployed to large-scale image retrieval due to its low storage cost and fast query speed. Almost all deep hashing methods do not sufficiently discover semantic correlation from label information, which results in the learned hash codes less discriminative. In this paper, we propose a novel Deep Joint Semantic-Embedding Hashing (DSEH) approach that contains LabNet and ImgNet. Specifically, LabNet is explored to capture abundant semantic correlation between sample pairs and supervise ImgNet from semantic level and hash codes level, which is conductive to the generated hash codes being more discriminative and similarity-preserving. Extensive experiments on three benchmark datasets show that the proposed model outperforms the state-of-the-art methods.

Efficient Multimedia Similarity Measurement Using Similar Elements

2019 ◽  
Author(s):  
Jun Long ◽  
Lei Zhu ◽  
Xinpan Yuan ◽  
Longzhi Sun
Keyword(s):  
Large Scale ◽  
Multimedia Data ◽  
Multimedia Retrieval ◽  
Similarity Measurement ◽  
Visual Words ◽  
Indexing Structure ◽  
Measurement Framework ◽  
The Arts ◽  
Definition Of ◽  
Similar Elements

Online social networking techniques and large-scale multimedia retrieval are developing rapidly, which not only has brought great convenience to our daily life, but generated, collected, and stored large-scale multimedia data as well. This trend has put forward higher requirements and greater challenges on massive multimedia retrieval. In this paper, we investigate the problem of image similarity measurement, which is one of the key problems of multimedia retrieval. Firstly, the definition of similarity measurement of images and the related notions are proposed. Then, an efficient similarity measurement framework is proposed. Besides, we present a novel basic method of similarity measurement named SMIN. To improve the performance of similarity measurement, we carefully design a novel indexing structure called SMI Temp Index (SMII for short). Moreover, we establish an index of potential similar visual words off-line to solve to problem that the index cannot be reused. Experimental evaluations on two real image datasets demonstrate that the proposed approach outperforms state-of-the-arts.

scholarly journals A Semantic Medical Multimedia Retrieval Approach Using Ontology Information Hiding

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Kehua Guo ◽  
Shigeng Zhang
Keyword(s):  
Semantic Information ◽  
Difficult Problem ◽  
Multimedia Data ◽  
Multimedia Retrieval ◽  
Multimedia Database ◽  
Good Precision ◽  
Multimedia Documents ◽  
Information Preservation ◽  
Medical Multimedia ◽  
Traditional Approaches

Searching useful information from unstructured medical multimedia data has been a difficult problem in information retrieval. This paper reports an effective semantic medical multimedia retrieval approach which can reflect the users’ query intent. Firstly, semantic annotations will be given to the multimedia documents in the medical multimedia database. Secondly, the ontology that represented semantic information will be hidden in the head of the multimedia documents. The main innovations of this approach are cross-type retrieval support and semantic information preservation. Experimental results indicate a good precision and efficiency of our approach for medical multimedia retrieval in comparison with some traditional approaches.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
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.

scholarly journals Similarity Preserving Deep Asymmetric Quantization for Image Retrieval

2019 ◽  
Vol 33 ◽  
pp. 8183-8190 ◽  
Author(s):  
Junjie Chen ◽  
William K. Cheung
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Multimedia Retrieval ◽  
Retrieval Performance ◽  
Retrieval Accuracy ◽  
Compact Binary ◽  
Label Information ◽  
Benchmark Datasets ◽  
Similarity Preserving ◽  
Novel Model

Quantization has been widely adopted for large-scale multimedia retrieval due to its effectiveness of coding highdimensional data. Deep quantization models have been demonstrated to achieve the state-of-the-art retrieval accuracy. However, training the deep models given a large-scale database is highly time-consuming as a large amount of parameters are involved. Existing deep quantization methods often sample only a subset from the database for training, which may end up with unsatisfactory retrieval performance as a large portion of label information is discarded. To alleviate this problem, we propose a novel model called Similarity Preserving Deep Asymmetric Quantization (SPDAQ) which can directly learn the compact binary codes and quantization codebooks for all the items in the database efficiently. To do that, SPDAQ makes use of an image subset as well as the label information of all the database items so the image subset items and the database items are mapped to two different but correlated distributions, where the label similarity can be well preserved. An efficient optimization algorithm is proposed for the learning. Extensive experiments conducted on four widely-used benchmark datasets demonstrate the superiority of our proposed SPDAQ model.

scholarly journals Graph Convolutional Network Hashing for Cross-Modal Retrieval

2019 ◽  
Author(s):  
Ruiqing Xu ◽  
Chao Li ◽  
Junchi Yan ◽  
Cheng Deng ◽  
Xianglong Liu
Keyword(s):  
Semantic Information ◽  
Convolutional Network ◽  
Retrieval Accuracy ◽  
Deep Architecture ◽  
Feature Encoding ◽  
Benchmark Datasets ◽  
Data Points ◽  
Similarity Structure ◽  
Main Components ◽  
Hash Codes

Deep network based cross-modal retrieval has recently made significant progress. However, bridging modality gap to further enhance the retrieval accuracy still remains a crucial bottleneck. In this paper, we propose a Graph Convolutional Hashing (GCH) approach, which learns modality-unified binary codes via an affinity graph. An end-to-end deep architecture is constructed with three main components: a semantic encoder module, two feature encoding networks, and a graph convolutional network (GCN). We design a semantic encoder as a teacher module to guide the feature encoding process, a.k.a. student module, for semantic information exploiting. Furthermore, GCN is utilized to explore the inherent similarity structure among data points, which will help to generate discriminative hash codes. Extensive experiments on three benchmark datasets demonstrate that the proposed GCH outperforms the state-of-the-art methods.

scholarly journals Large Scale Multimedia Management: A Comprehensive Review

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 28
Author(s):  
Saïd Mahmoudi ◽  
Mohammed Amin Belarbi
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Large Datasets ◽  
Multimedia Data ◽  
Multimedia Retrieval ◽  
Easy Access ◽  
3D Images ◽  
Comprehensive Review ◽  
Large Databases ◽  
Multimedia Management

Multimedia applications deal, in most cases, with an extremely high volume of multimedia data (2D and 3D images, sounds, videos). That is why efficient algorithms should be developed to analyze and process these large datasets. On the other hand, multimedia management is based on efficient representation of knowledge which allows efficient data processing and retrieval. The main challenge in this era is to achieve clever and quick access to these huge datasets to allow easy access to the data and in a reasonable time. In this context, large-scale image retrieval is a fundamental task. Many methods have been developed in the literature to achieve fast and efficient navigating in large databases by using the famous content-based image retrieval (CBIR) methods associated with these methods allowing a decrease in the computing time, such as dimensional reduction and hashing methods. More recently, these methods based on convolutional neural networks (CNNs) for feature extraction and image classification are widely used. In this paper, we present a comprehensive review of recent multimedia retrieval methods and algorithms applied to large datasets of 2D/3D images and videos. This editorial paper discusses the mains challenges of multimedia retrieval in a context of large databases.

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