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A novel cross-modal hashing algorithm based on multimodal deep learning

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Science China(Information Sciences) 2017年第9期60卷 50-63页

作者： Wen QU Daling WANG Shi FENG Yifei ZHANG Ge YU School of Information Science and Engineering Northeastern University Key Laboratory of Medical Image Computing

With the growing popularity of multimodal data on the Web, cross-modal retrieval on large-scale multimedia databases has become an important research topic. Cross-modal retrieval methods based on hashing assume that there is a latent space shared by multimodal features. To model the relationship among heterogeneous data, most existing methods embed the data into a joint abstraction space by linear projections. However,these approaches are sensitive to noise in the data and are unable to make use of unlabeled data and multimodal data with missing values in real-world applications. To address these challenges, we proposed a novel multimodal deep-learning-based hash(MDLH) algorithm. In particular, MDLH uses a deep neural network to encode heterogeneous features into a compact common representation and learns the hash functions based on the common representation. The parameters of the whole model are fine-tuned in a supervised training stage.Experiments on two standard datasets show that the method achieves more effective results than other methods in cross-modal retrieval.

关键词： hashing cross-modal retrieval cross-modal hashing multimodal data analysis deep learning

Orientation Field Code hashing:A Novel Method for Fast Palmprint Identification

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IEEE/CAA Journal of Automatica Sinica 2021年第5期8卷 1038-1051页

作者： Xi Chen Ming Yu Feng Yue Bin Li the School of Electronic and Information Engineering Hebei University of Technology(HEBUT)Tianjin 300401 Beijing Institute of New Technology Applications Beijing Academy of Sciences and TechnologyBeijing 100035China the School of Articial Intelligence Hebei University of Technology(HEBUT)Beijing 100035China

For a large-scale palmprint identification system,it is necessary to speed up the identification process to reduce the response time and also to have a high rate of identification accuracy.In this paper,we propose a novel hashing-based technique called orientation field code hashing for fast palmprint identification.By investigating hashing-based algorithms,we first propose a double-orientation encoding method to eliminate the instability of orientation codes and make the orientation codes more reasonable.Secondly,we propose a window-based feature measurement for rapid searching of the target.We explore the influence of parameters related to hashing-based palmprint identification.We have carried out a number of experiments on the Hong Kong Poly U large-scale database and the CASIA palmprint database plus a synthetic database.The results show that on the Hong Kong Poly U large-scale database,the proposed method is about 1.5 times faster than the state-of-the-art ones,while achieves the comparable identification accuracy.On the CASIA database plus the synthetic database,the proposed method also achieves a better performance on identification speed.

关键词： Biometric system hashing orientation feature palmprint identification

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Fast Near-duplicate Image Detection in Riemannian Space by A Novel hashing Scheme

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Computers, Materials & Continua 2018年第9期56卷 529-539页

作者： Ligang Zheng Chao Song Guangzhou University Guangzhou High Education Mega CenterGuangzhou510006China Illinois Institute of Technology 3440 South DearbornSuit 100ChicagoIL60616USA

There is a steep increase in data encoded as symmetric positive definite(SPD)matrix in the past decade.The set of SPD matrices forms a Riemannian manifold that constitutes a half convex cone in the vector space of matrices,which we sometimes call SPD manifold.One of the fundamental problems in the application of SPD manifold is to find the nearest neighbor of a queried SPD matrix.hashing is a popular method that can be used for the nearest neighbor search.However,hashing cannot be directly applied to SPD manifold due to its non-Euclidean intrinsic geometry.Inspired by the idea of kernel trick,a new hashing scheme for SPD manifold by random projection and quantization in expanded data space is proposed in this paper.Experimental results in large scale nearduplicate image detection show the effectiveness and efficiency of the proposed method.

关键词： Riemannian manifold congruent transformation hashing kernel trick

Cross-Modal hashing Retrieval Based on Deep Residual Network

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Computer Systems Science & Engineering 2021年第2期36卷 383-405页

作者： Zhiyi Li Xiaomian Xu Du Zhang Peng Zhang Faculty of Information Technology Macao University of Science and TechnologyMacao School of Economics and Management South China Normal UniversityGuangzhou510006China

In the era of big data rich inWe Media,the single mode retrieval system has been unable to meet people’s demand for information retrieval.This paper proposes a new solution to the problem of feature extraction and unified mapping of different modes:A Cross-Modal hashing retrieval algorithm based on Deep Residual Network(CMHR-DRN).The model construction is divided into two stages:The first stage is the feature extraction of different modal data,including the use of Deep Residual Network(DRN)to extract the image features,using the method of combining TF-IDF with the full connection network to extract the text features,and the obtained image and text features used as the input of the second stage.In the second stage,the image and text features are mapped into Hash functions by supervised learning,and the image and text features are mapped to the common binary Hamming space.In the process of mapping,the distance measurement of the original distance measurement and the common feature space are kept unchanged as far as possible to improve the accuracy of Cross-Modal Retrieval.In training the model,adaptive moment estimation(Adam)is used to calculate the adaptive learning rate of each parameter,and the stochastic gradient descent(SGD)is calculated to obtain the minimum loss function.The whole training process is completed on Caffe deep learning framework.Experiments show that the proposed algorithm CMHR-DRN based on Deep Residual Network has better retrieval performance and stronger advantages than other Cross-Modal algorithms CMFH,CMDN and CMSSH.

关键词： Deep residual network cross-modal retrieval hashing cross-modal hashing retrieval based on deep residual network

Feature Fusion Multi-View hashing Based on Random Kernel Canonical Correlation Analysis

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Computers, Materials & Continua 2020年第5期63卷 675-689页

作者： Junshan Tan Rong Duan Jiaohua Qin Xuyu Xiang Yun Tan College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410114China

hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system,making it more and more widely used in image retrieval.Multi-view data describes image information more comprehensively than traditional methods using a single-view.How to use hashing to combine multi-view data for image retrieval is still a challenge.In this paper,a multi-view fusion hashing method based on RKCCA(Random Kernel Canonical Correlation Analysis)is proposed.In order to describe image content more accurately,we use deep learning dense convolutional network feature DenseNet to construct multi-view by combining GIST feature or BoW_SIFT(Bag-of-Words model+SIFT feature)feature.This algorithm uses RKCCA method to fuse multi-view features to construct association features and apply them to image retrieval.The algorithm generates binary hash code with minimal distortion error by designing quantization regularization terms.A large number of experiments on benchmark datasets show that this method is superior to other multi-view hashing methods.

关键词： hashing multi-view data random kernel canonical correlation analysis feature fusion deep learning

Homomorphic hashing Verification for Wireless Sensor Networks Rateless Codes Over-the-Air Programming

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Journal of Beijing Institute of Technology 2019年第1期28卷 109-118页

作者： Hao He Weidong Yi Ming Li Yongrui Chen School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of Sciences

The homomorphic hash algorithm(HHA)is introduced to help on-the-fly verify the vireless sensor network(WSN)over-the-air programming(OAP)data based on rateless codes.The receiver calculates the hash value of a group of data by homomorphic hash function,and then it compares the hash value with the receiving message digest.Because the feedback channel is deliberately removed during the distribution process,the rateless codes are often vulnerable when they face security issues such as packets contamination or attack.This method prevents contaminating or attack on rateless codes and reduces the potential risks of decoding failure.Compared with the SHA1 and MD5,HHA,which has a much shorter message digest,will deliver more data.The simulation results show that to transmit and verify the same amount of OAP data,HHA method sends 17.9% to 23.1%fewer packets than MD5 and SHA1 under different packet loss rates.

关键词： wireless sensor network(WSN) over-the-air programming rateless codes homomorphic hashing security

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Lung Nodule Image Retrieval Based on Convolutional Neural Networks and hashing

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Journal of Beijing Institute of Technology 2019年第1期28卷 17-26页

作者： Yan Qiang Xiaolan Yang Juanjuan Zhao Qiang Cui Xiaoping Du College of Computer Science and Technology Taiyuan University of Science and Technology Shanxi Coal Central Hospital

Lung medical image retrieval based on content similarity plays an important role in computer-aided diagnosis of lung cancer.In recent years,binary hashing has become a hot topic in this field due to its compressed storage and fast query speed.Traditional hashing methods often rely on highdimensional features based hand-crafted methods,which might not be optimally compatible with lung nodule images.Also,different hashing bits contribute to the image retrieval differently,and therefore treating the hashing bits equally affects the retrieval accuracy.Hence,an image retrieval method of lung nodule images is proposed with the basis on convolutional neural networks and hashing.First,apre-trained and fine-tuned convolutional neural network is employed to learn multilevel semantic features of the lung nodules.Principal components analysis is utilized to remove redundant information and preserve informative semantic features of the lung nodules.Second,the proposed method relies on nine sign labels of lung nodules for the training set,and the semantic feature is combined to construct hashing functions.Finally,returned lung nodule images can be easily ranked with the query-adaptive search method based on weighted Hamming distance.Extensive experiments and evaluations on the dataset demonstrate that the proposed method can significantly improve the expression ability of lung nodule images,which further validates the effectiveness of the proposed method.

关键词： lung nodule image retrieval convolutional neural networks informative semantic features hashing

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Fusion of Hash-Based Hard and Soft Biometrics for Enhancing Face Image Database Search and Retrieval

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Computers, Materials & Continua 2023年第12期77卷 3489-3509页

作者： Ameerah Abdullah Alshahrani Emad Sami Jaha Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade,owing to the continuing advances in image processing and computer vision approaches.In multiple real-life applications,for example,social media,content-based face picture retrieval is a well-invested technique for large-scale databases,where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures.Humans widely employ faces for recognizing and identifying people.Thus,face recognition through formal or personal pictures is increasingly used in various real-life applications,such as helping crime investigators retrieve matching images from face image databases to identify victims and criminals.However,such face image retrieval becomes more challenging in large-scale databases,where traditional vision-based face analysis requires ample additional storage space than the raw face images already occupied to store extracted lengthy feature vectors and takes much longer to process and match thousands of face images.This work mainly contributes to enhancing face image retrieval performance in large-scale databases using hash codes inferred by locality-sensitive hashing(LSH)for facial hard and soft biometrics as(Hard BioHash)and(Soft BioHash),respectively,to be used as a search input for retrieving the top-k matching faces.Moreover,we propose the multi-biometric score-level fusion of both face hard and soft BioHashes(Hard-Soft BioHash Fusion)for further augmented face image retrieval.The experimental outcomes applied on the Labeled Faces in the Wild(LFW)dataset and the related attributes dataset(LFW-attributes),demonstrate that the retrieval performance of the suggested fusion approach(Hard-Soft BioHash Fusion)significantly improved the retrieval performance compared to solely using Hard BioHash or Soft BioHash in isolation,where the suggested method provides an augmented accuracy of 87%when executed on 1000 specimens

关键词： Face image retrieval soft biometrics similar pictures hashing database search large databases score-level fusion multimodal fusion

Deep Root Memory Optimized Indexing Methodology for Image Search Engines

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Computer Systems Science & Engineering 2022年第2期40卷 661-672页

作者： R.Karthikeyan A.Celine Kavida P.Suresh Department CSE Vel Tech HighTech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062TamilnaduIndia Department of Physics Vel Tech Multitech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062Tamil NaduIndia Department of ECE Veltech Rangarajan Dr Sagunthala R&D Institute of Science and TechnologyChennaiTamilnadu600062India

Digitization has created an abundance of new information sources by altering how pictures are captured.Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing.This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets.Image retrieval usually encounters difficulties like a)merging the diverse representations of images and their Indexing,b)the low-level visual characters and semantic characters associated with an image are indirectly proportional,and c)noisy and less accurate extraction of image information(semantic and predicted attributes).This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively.Thus,retrieval becomes straightforward and rapid.This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost.We focus on the schema design on a non-clustered index solution,especially cover queries.This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing.Finally,we include non-key columns in addition to the key columns.Experiments on two image data sets‘with and without’filtered indexing show low query cost.We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing.The results show that retrieval by using deep root indexing is simple and fast.

关键词： Multi-dimensional indexing deep root hashing image retrieval filtered indexing