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.

Detection of Botnet Based Attacks on Network

2018 ◽  
pp. 101-116
Author(s):  
Prachi
Keyword(s):  
Large Scale ◽  
Flow Analysis ◽  
Traffic Analysis ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Botnet Detection ◽  
Learning Techniques ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Traffic Flow Analysis

This chapter describes how with Botnets becoming more and more the leading cyber threat on the web nowadays, they also serve as the key platform for carrying out large-scale distributed attacks. Although a substantial amount of research in the fields of botnet detection and analysis, bot-masters inculcate new techniques to make them more sophisticated, destructive and hard to detect with the help of code encryption and obfuscation. This chapter proposes a new model to detect botnet behavior on the basis of traffic analysis and machine learning techniques. Traffic analysis behavior does not depend upon payload analysis so the proposed technique is immune to code encryption and other evasion techniques generally used by bot-masters. This chapter analyzes the benchmark datasets as well as real-time generated traffic to determine the feasibility of botnet detection using traffic flow analysis. Experimental results clearly indicate that a proposed model is able to classify the network traffic as a botnet or as normal traffic with a high accuracy and low false-positive rates.

A Combination of Spatial Pyramid and Inverted Index for Large-Scale Image Retrieval

2018 ◽  
pp. 1307-1321
Author(s):  
Vinh-Tiep Nguyen ◽  
Thanh Duc Ngo ◽  
Minh-Triet Tran ◽  
Duy-Dinh Le ◽  
Duc Anh Duong
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Spatial Information ◽  
Real Life ◽  
Inverted Index ◽  
Bag Of Words ◽  
Visual Words ◽  
Benchmark Datasets ◽  
Large Scale Image Retrieval ◽  
Inverted Indexing

Large-scale image retrieval has been shown remarkable potential in real-life applications. The standard approach is based on Inverted Indexing, given images are represented using Bag-of-Words model. However, one major limitation of both Inverted Index and Bag-of-Words presentation is that they ignore spatial information of visual words in image presentation and comparison. As a result, retrieval accuracy is decreased. In this paper, the authors investigate an approach to integrate spatial information into Inverted Index to improve accuracy while maintaining short retrieval time. Experiments conducted on several benchmark datasets (Oxford Building 5K, Oxford Building 5K+100K and Paris 6K) demonstrate the effectiveness of our proposed approach.

A Combination of Spatial Pyramid and Inverted Index for Large-Scale Image Retrieval

2015 ◽  
Vol 6 (2) ◽  
pp. 37-51 ◽  
Author(s):  
Vinh-Tiep Nguyen ◽  
Thanh Duc Ngo ◽  
Minh-Triet Tran ◽  
Duy-Dinh Le ◽  
Duc Anh Duong
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Spatial Information ◽  
Real Life ◽  
Inverted Index ◽  
Bag Of Words ◽  
Visual Words ◽  
Benchmark Datasets ◽  
Large Scale Image Retrieval ◽  
Inverted Indexing

Large-scale image retrieval has been shown remarkable potential in real-life applications. The standard approach is based on Inverted Indexing, given images are represented using Bag-of-Words model. However, one major limitation of both Inverted Index and Bag-of-Words presentation is that they ignore spatial information of visual words in image presentation and comparison. As a result, retrieval accuracy is decreased. In this paper, the authors investigate an approach to integrate spatial information into Inverted Index to improve accuracy while maintaining short retrieval time. Experiments conducted on several benchmark datasets (Oxford Building 5K, Oxford Building 5K+100K and Paris 6K) demonstrate the effectiveness of our proposed approach.

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 Graph LSTM with Context-Gated Mechanism for Spoken Language Understanding

2020 ◽  
Vol 34 (05) ◽  
pp. 9539-9546
Author(s):  
Linhao Zhang ◽  
Dehong Ma ◽  
Xiaodong Zhang ◽  
Xiaohui Yan ◽  
Houfeng Wang
Keyword(s):  
Message Passing ◽  
Spoken Language ◽  
Language Understanding ◽  
Semantic Correlation ◽  
Spoken Language Understanding ◽  
Extensive Evaluation ◽  
Proposed Model ◽  
Sequential Models ◽  
Almost All ◽  
Slot Filling

Much research in recent years has focused on spoken language understanding (SLU), which usually involves two tasks: intent detection and slot filling. Since Yao et al.(2013), almost all SLU systems are RNN-based, which have been shown to suffer various limitations due to their sequential nature. In this paper, we propose to tackle this task with Graph LSTM, which first converts text into a graph and then utilizes the message passing mechanism to learn the node representation. Not only the Graph LSTM addresses the limitations of sequential models, but it can also help to utilize the semantic correlation between slot and intent. We further propose a context-gated mechanism to make better use of context information for slot filling. Our extensive evaluation shows that the proposed model outperforms the state-of-the-art results by a large margin.

scholarly journals Multi-task learning for Chinese clinical named entity recognition with external knowledge

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ming Cheng ◽  
Shufeng Xiong ◽  
Fei Li ◽  
Pan Liang ◽  
Jianbo Gao
Keyword(s):  
Large Scale ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Data Driven ◽  
Named Entity ◽  
Proposed Model ◽  
Comparison Results ◽  
Benchmark Datasets ◽  
Public Datasets ◽  
F Measure

Abstract Background Named entity recognition (NER) on Chinese electronic medical/healthcare records has attracted significantly attentions as it can be applied to building applications to understand these records. Most previous methods have been purely data-driven, requiring high-quality and large-scale labeled medical data. However, labeled data is expensive to obtain, and these data-driven methods are difficult to handle rare and unseen entities. Methods To tackle these problems, this study presents a novel multi-task deep neural network model for Chinese NER in the medical domain. We incorporate dictionary features into neural networks, and a general secondary named entity segmentation is used as auxiliary task to improve the performance of the primary task of named entity recognition. Results In order to evaluate the proposed method, we compare it with other currently popular methods, on three benchmark datasets. Two of the datasets are publicly available, and the other one is constructed by us. Experimental results show that the proposed model achieves 91.07% average f-measure on the two public datasets and 87.05% f-measure on private dataset. Conclusions The comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models.

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 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 Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding

2019 ◽  
Vol 33 ◽  
pp. 8489-8496 ◽  
Author(s):  
Yan Huang ◽  
Yang Long ◽  
Liang Wang
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Similarity Metrics ◽  
Relative Importance ◽  
Challenging Problem ◽  
Semantic Embedding ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
The Common ◽  
Sentence Matching

Although image and sentence matching has been widely studied, its intrinsic few-shot problem is commonly ignored, which has become a bottleneck for further performance improvement. In this work, we focus on this challenging problem of few-shot image and sentence matching, and propose a Gated Visual-Semantic Embedding (GVSE) model to deal with it. The model consists of three corporative modules in terms of uncommon VSE, common VSE, and gated metric fusion. The uncommon VSE exploits external auxiliary resources to extract generic features for representing uncommon instances and words in images and sentences, and then integrates them by modeling their semantic relation to obtain global representations for association analysis. To better model other common instances and words in rest content of images and sentences, the common VSE learns their discriminative representations directly from scratch. After obtaining two similarity metrics from the two VSE modules with different advantages, the gated metric fusion module adaptively fuses them by automatically balancing their relative importance. Based on the fused metric, we perform extensive experiments in terms of few-shot and conventional image and sentence matching, and demonstrate the effectiveness of the proposed model by achieving the state-of-the-art results on two public benchmark datasets.

scholarly journals Progressive Generative Hashing for Image Retrieval

2018 ◽  
Author(s):  
Yuqing Ma ◽  
Yue He ◽  
Fan Ding ◽  
Sheng Hu ◽  
Jun Li ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Generative Adversarial Networks ◽  
Learning Capacity ◽  
Deep Hashing ◽  
Adversarial Networks ◽  
Label Information ◽  
Semantic Label ◽  
Large Scale Image Retrieval ◽  
Open Question

Recent years have witnessed the success of the emerging hashing techniques in large-scale image retrieval. Owing to the great learning capacity, deep hashing has become one of the most promising solutions, and achieved attractive performance in practice. However, without semantic label information, the unsupervised deep hashing still remains an open question. In this paper, we propose a novel progressive generative hashing (PGH) framework to help learn a discriminative hashing network in an unsupervised way. Very different from existing studies, it first treats the hash codes as a kind of semantic condition for the similar image generation, and simultaneously feeds the original image and its codes into the generative adversarial networks (GANs). The real images together with the synthetic ones can further help train a discriminative hashing network based on a triplet loss. By iteratively inputting the learnt codes into the hash conditioned GANs, we can progressively enable the hashing network to discover the semantic relations. Extensive experiments on the widely-used image datasets demonstrate that PGH can significantly outperforms state-of-the-art unsupervised hashing methods.

Sign in / Sign up

Export Citation Format

Share Document