scholarly journals Jointly Multiple Hash Learning

2019 ◽  
Vol 33 ◽  
pp. 9981-9982
Author(s):  
Xingbo Liu ◽  
Xiushan Nie ◽  
Yingxin Wang ◽  
Yilong Yin
Keyword(s):  
Superior Performance ◽  
Closed Form Solutions ◽  
Fixed Length ◽  
Compact Binary ◽  
Proposed Model ◽  
Efficient Retrieval ◽  
Benchmark Datasets ◽  
One Step ◽  
Types Of Information ◽  
Hash Codes

Hashing can compress heterogeneous high-dimensional data into compact binary codes while preserving the similarity to facilitate efficient retrieval and storage, and thus hashing has recently received much attention from information retrieval researchers. Most of the existing hashing methods first predefine a fixed length (e.g., 32, 64, or 128 bit) for the hash codes before learning them with this fixed length. However, one sample can be represented by various hash codes with different lengths, and thus there must be some associations and relationships among these different hash codes because they represent the same sample. Therefore, harnessing these relationships will boost the performance of hashing methods. Inspired by this possibility, in this study, we propose a new model jointly multiple hash learning (JMH), which can learn hash codes with multiple lengths simultaneously. In the proposed JMH method, three types of information are used for hash learning, which come from hash codes with different lengths, the original features of the samples and label. In contrast to the existing hashing methods, JMH can learn hash codes with different lengths in one step. Users can select appropriate hash codes for their retrieval tasks according to the requirements in terms of accuracy and complexity. To the best of our knowledge, JMH is one of the first attempts to learn multi-length hash codes simultaneously. In addition, in the proposed model, discrete and closed-form solutions for variables can be obtained by cyclic coordinate descent, thereby making the proposed model much faster during training. Extensive experiments were performed based on three benchmark datasets and the results demonstrated the superior performance of the proposed method.

scholarly journals Supervised Short-Length Hashing

2019 ◽  
Author(s):  
Xingbo Liu ◽  
Xiushan Nie ◽  
Quan Zhou ◽  
Xiaoming Xi ◽  
Lei Zhu ◽  
...  
Keyword(s):  
Short Length ◽  
Superior Performance ◽  
High Dimensional ◽  
Robust Estimator ◽  
Compact Binary ◽  
Hash Code ◽  
Label Information ◽  
Efficient Retrieval ◽  
And Storage ◽  
Hash Codes

Hashing can compress high-dimensional data into compact binary codes, while preserving the similarity, to facilitate efficient retrieval and storage. However, when retrieving using an extremely short length hash code learned by the existing methods, the performance cannot be guaranteed because of severe information loss. To address this issue, in this study, we propose a novel supervised short-length hashing (SSLH). In this proposed SSLH, mutual reconstruction between the short-length hash codes and original features are performed to reduce semantic loss. Furthermore, to enhance the robustness and accuracy of the hash representation, a robust estimator term is added to fully utilize the label information. Extensive experiments conducted on four image benchmarks demonstrate the superior performance of the proposed SSLH with short-length hash codes. In addition, the proposed SSLH outperforms the existing methods, with long-length hash codes. To the best of our knowledge, this is the first linear-based hashing method that focuses on both short and long-length hash codes for maintaining high precision.

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.

scholarly journals Scalable supervised online hashing for image retrieval

2021 ◽  
Vol 8 (5) ◽  
pp. 1391-1406
Author(s):  
Yuzhi Fang ◽  
Li Liu
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Superior Performance ◽  
Binary Codes ◽  
Retrieval Performance ◽  
Unified Framework ◽  
Retrieval Task ◽  
Compact Binary ◽  
Hash Codes ◽  
Existing Data

Abstract Online hashing methods aim to learn compact binary codes of the new data stream, and update the hash function to renew the codes of the existing data. However, the addition of new data streams has a vital impact on the retrieval performance of the entire retrieval system, especially the similarity measurement between new data streams and existing data, which has always been one of the focuses of online retrieval research. In this paper, we present a novel scalable supervised online hashing method, to solve the above problems within a unified framework. Specifically, the similarity matrix is established by the label matrix of the existing data and the new data stream. The projection of the existing data label matrix is then used as an intermediate term to approximate the binary codes of the existing data, which not only realizes the semantic information of the hash codes learning but also effectively alleviates the problem of data imbalance. In addition, an alternate optimization algorithm is proposed to efficiently make the solution of the model. Extensive experiments on three widely used datasets validate its superior performance over several state-of-the-art methods in terms of both accuracy and scalability for online retrieval task.

scholarly journals Classification and pattern extraction of incidents: a deep learning-based approach

2022 ◽  
Author(s):  
Sobhan Sarkar ◽  
Sammangi Vinay ◽  
Chawki Djeddi ◽  
J. Maiti
Keyword(s):  
Superior Performance ◽  
Hidden Information ◽  
Text Data ◽  
Moment Estimation ◽  
The Arts ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Unexplored Area ◽  
Key Terms

AbstractClassifying or predicting occupational incidents using both structured and unstructured (text) data are an unexplored area of research. Unstructured texts, i.e., incident narratives are often unutilized or underutilized. Besides the explicit information, there exist a large amount of hidden information present in a dataset, which cannot be explored by the traditional machine learning (ML) algorithms. There is a scarcity of studies that reveal the use of deep neural networks (DNNs) in the domain of incident prediction, and its parameter optimization for achieving better prediction power. To address these issues, initially, key terms are extracted from the unstructured texts using LDA-based topic modeling. Then, these key terms are added with the predictor categories to form the feature vector, which is further processed for noise reduction and fed to the adaptive moment estimation (ADAM)-based DNN (i.e., ADNN) for classification, as ADAM is superior to GD, SGD, and RMSProp. To evaluate the effectiveness of our proposed method, a comparative study has been conducted using some state-of-the-arts on five benchmark datasets. Moreover, a case study of an integrated steel plant in India has been demonstrated for the validation of the proposed model. Experimental results reveal that ADNN produces superior performance than others in terms of accuracy. Therefore, the present study offers a robust methodological guide that enables us to handle the issues of unstructured data and hidden information for developing a predictive model.

Incorporating Biterm Correlation Knowledge into Topic Modeling for Short Texts

2020 ◽  
Author(s):  
Kai Zhang ◽  
Yuan Zhou ◽  
Zheng Chen ◽  
Yufei Liu ◽  
Zhuo Tang ◽  
...  
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Semantic Knowledge ◽  
Superior Performance ◽  
Knowledge Based ◽  
Modeling Process ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Latent Topic

Abstract The prevalence of short texts on the Web has made mining the latent topic structures of short texts a critical and fundamental task for many applications. However, due to the lack of word co-occurrence information induced by the content sparsity of short texts, it is challenging for traditional topic models like latent Dirichlet allocation (LDA) to extract coherent topic structures on short texts. Incorporating external semantic knowledge into the topic modeling process is an effective strategy to improve the coherence of inferred topics. In this paper, we develop a novel topic model—called biterm correlation knowledge-based topic model (BCK-TM)—to infer latent topics from short texts. Specifically, the proposed model mines biterm correlation knowledge automatically based on recent progress in word embedding, which can represent semantic information of words in a continuous vector space. To incorporate external knowledge, a knowledge incorporation mechanism is designed over the latent topic layer to regularize the topic assignment of each biterm during the topic sampling process. Experimental results on three public benchmark datasets illustrate the superior performance of the proposed approach over several state-of-the-art baseline models.

Cross-modal variable-length hashing based on hierarchy

2021 ◽  
Vol 25 (3) ◽  
pp. 669-685
Author(s):  
Xiaojun Qi ◽  
Xianhua Zeng ◽  
Shumin Wang ◽  
Yicai Xie ◽  
Liming Xu
Keyword(s):  
Hash Functions ◽  
Variable Length ◽  
Superior Performance ◽  
Hierarchical Architecture ◽  
Training Time ◽  
Computational Costs ◽  
Research Fields ◽  
Efficient Retrieval ◽  
Benchmark Datasets ◽  
And Training

Due to the emergence of the era of big data, cross-modal learning have been applied to many research fields. As an efficient retrieval method, hash learning is widely used frequently in many cross-modal retrieval scenarios. However, most of existing hashing methods use fixed-length hash codes, which increase the computational costs for large-size datasets. Furthermore, learning hash functions is an NP hard problem. To address these problems, we initially propose a novel method named Cross-modal Variable-length Hashing Based on Hierarchy (CVHH), which can learn the hash functions more accurately to improve retrieval performance, and also reduce the computational costs and training time. The main contributions of CVHH are: (1) We propose a variable-length hashing algorithm to improve the algorithm performance; (2) We apply the hierarchical architecture to effectively reduce the computational costs and training time. To validate the effectiveness of CVHH, our extensive experimental results show the superior performance compared with recent state-of-the-art cross-modal methods on three benchmark datasets, WIKI, NUS-WIDE and MIRFlickr.

scholarly journals Neural Network Modelling of Speech Emotion Detection

2021 ◽  
Vol 309 ◽  
pp. 01139
Author(s):  
Y. Sri Lalitha ◽  
Althaf Hussain Basha Sk ◽  
M. V. Aditya Nag
Keyword(s):  
Speech Recognition ◽  
Point Of View ◽  
Emotion Detection ◽  
Network Modelling ◽  
Emotion Identification ◽  
Essential Requirement ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Types Of Information ◽  
Improved Accuracy

In making the Machines Intelligent, and enable them to work as human, Speech recognition is one of the most essential requirement. Human Language conveys various types of information such as the energy, pitch, loudness, rhythm etc., in the sound, the speech and its context such as gender, age and the emotion. Identifying the emotion from a speech pattern is a challenging task and the most useful solution especially in the era of widely developing speech recognition systems with digital assistants. Digital assistants like Bixby, Blackberry assistant are building products that consist of emotion identification and reply the user in step with user point of view. The objective of this work is to improve the accuracy of the speech emotion prediction using deep learning models. Our work experiments with the MLP and CNN classification models on three benchmark datasets with 5700 speech files of 7 emotion categories. The proposed model showed improved accuracy.

scholarly journals A novel fully convolutional network for visual saliency prediction

2020 ◽  
Vol 6 ◽  
pp. e280
Author(s):  
Bashir Muftah Ghariba ◽  
Mohamed S. Shehata ◽  
Peter McGuire
Keyword(s):  
Deep Learning ◽  
Visual Saliency ◽  
Superior Performance ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Proposed Model ◽  
Saliency Prediction ◽  
Benchmark Datasets ◽  
The Many ◽  
Deep Learning Model

A human Visual System (HVS) has the ability to pay visual attention, which is one of the many functions of the HVS. Despite the many advancements being made in visual saliency prediction, there continues to be room for improvement. Deep learning has recently been used to deal with this task. This study proposes a novel deep learning model based on a Fully Convolutional Network (FCN) architecture. The proposed model is trained in an end-to-end style and designed to predict visual saliency. The entire proposed model is fully training style from scratch to extract distinguishing features. The proposed model is evaluated using several benchmark datasets, such as MIT300, MIT1003, TORONTO, and DUT-OMRON. The quantitative and qualitative experiment analyses demonstrate that the proposed model achieves superior performance for predicting visual saliency.

scholarly journals Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Narjes Rohani ◽  
Changiz Eslahchi
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Side Effect ◽  
Network Architecture ◽  
Selection Process ◽  
Superior Performance ◽  
Multiple Drug ◽  
Interaction Prediction ◽  
Benchmark Datasets ◽  
Drug Drug Interaction

Abstract Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD.

scholarly journals An efficient pruning scheme of deep neural networks for Internet of Things applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chen Qi ◽  
Shibo Shen ◽  
Rongpeng Li ◽  
Zhifeng Zhao ◽  
Qing Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Internet Of Things ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Compact Structure ◽  
Resource Limited ◽  
Benchmark Datasets ◽  
Iot Devices

AbstractNowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.

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