scholarly journals High-Resolution Remote Sensing Image Integrity Authentication Method Considering Both Global and Local Features

2020 ◽  
Vol 9 (4) ◽  
pp. 254
Author(s):  
Xingang Zhang ◽  
Haowen Yan ◽  
Liming Zhang ◽  
Hao Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Local Features ◽  
Tamper Localization ◽  
Global Features ◽  
Key Points ◽  
Recognition Ability ◽  
Decision Making Processes ◽  
Image Integrity ◽  
Global And Local

Content integrity of high-resolution remote sensing (HRRS) images is the premise of its usability. Existing HRRS image integrity authentication methods are mostly binary decision-making processes, which cannot provide a further interpretable information (e.g., tamper localization, tamper type determination). Due to this reason, a robust HRRS images integrity authentication algorithm using perceptual hashing technology considering both global and local features is proposed in this paper. It extracts global features by the efficient recognition ability of Zernike moments to texture information. Meanwhile, Features from Accelerated Segment Test (FAST) key points are applied to local features construction and tamper localization. By applying the concept of multi-feature combination to the integrity authentication of HRRS images, the authentication process is more convincing in comparison to existing algorithms. Furthermore, an interpretable authentication result can be given. The experimental results show that the algorithm proposed in this paper is highly robust to the content retention operation, has a strong sensitivity to the content changing operations, and the result of tampering localization is more precise comparing with existing algorithms.

scholarly journals Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation

2021 ◽  
Vol 13 (22) ◽  
pp. 4518
Author(s):  
Xin Zhao ◽  
Jiayi Guo ◽  
Yueting Zhang ◽  
Yirong Wu
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Semantic Segmentation ◽  
Local Features ◽  
Local Feature ◽  
Global Information ◽  
Deep Convolutional Neural Networks ◽  
Global Representation ◽  
Local Feature Extraction ◽  
Global And Local

The semantic segmentation of remote sensing images requires distinguishing local regions of different classes and exploiting a uniform global representation of the same-class instances. Such requirements make it necessary for the segmentation methods to extract discriminative local features between different classes and to explore representative features for all instances of a given class. While common deep convolutional neural networks (DCNNs) can effectively focus on local features, they are limited by their receptive field to obtain consistent global information. In this paper, we propose a memory-augmented transformer (MAT) to effectively model both the local and global information. The feature extraction pipeline of the MAT is split into a memory-based global relationship guidance module and a local feature extraction module. The local feature extraction module mainly consists of a transformer, which is used to extract features from the input images. The global relationship guidance module maintains a memory bank for the consistent encoding of the global information. Global guidance is performed by memory interaction. Bidirectional information flow between the global and local branches is conducted by a memory-query module, as well as a memory-update module, respectively. Experiment results on the ISPRS Potsdam and ISPRS Vaihingen datasets demonstrated that our method can perform competitively with state-of-the-art methods.

scholarly journals BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images

2020 ◽  
Vol 12 (6) ◽  
pp. 1050 ◽  
Author(s):  
Zhenfeng Shao ◽  
Penghao Tang ◽  
Zhongyuan Wang ◽  
Nayyer Saleem ◽  
Sarath Yam ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Input Image ◽  
Economic Forecast ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Global Features ◽  
Prediction Module ◽  
Deep Learning Network ◽  
The One

Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder–decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints.

INFLUENCE OF mRNA FEATURES ON siRNA INTERFERENCE EFFICACY

2013 ◽  
Vol 11 (03) ◽  
pp. 1341004 ◽  
Author(s):  
YUANNING LIU ◽  
YAPING CHANG ◽  
CHAO ZHANG ◽  
QINGKAI WEI ◽  
JINGBO CHEN ◽  
...  
Keyword(s):  
Binding Site ◽  
Gc Content ◽  
Prediction Method ◽  
Secondary Structures ◽  
Local Features ◽  
Global Features ◽  
Small Interference Rna ◽  
Rnai Technology ◽  
Sirna Design ◽  
Global And Local

Design of small interference RNA (siRNA) is one of the most important steps in effectively applying the RNA interference (RNAi) technology. The current siRNA design often produces inconsistent design results, which often fail to reliably select siRNA with clear silencing effects. We propose that when designing siRNA, one should consider mRNA global features and near siRNA-binding site local features. By a linear regression study, we discovered strong correlations between inhibitory efficacy and both mRNA global features and neighboring local features. This paper shows that, on average, less GC content, fewer stem secondary structures, and more loop secondary structures of mRNA at both global and local flanking regions of the siRNA binding sites lead to stronger inhibitory efficacy. Thus, the use of mRNA global features and near siRNA-binding site local features are essential to successful gene silencing and hence, a better siRNA design. We use a random forest model to predict siRNA efficacy using siRNA features, mRNA features, and near siRNA binding site features. Our prediction method achieved a correlation coefficient of 0.7 in 10-fold cross validation in contrast to 0.63 when using siRNA features only. Our study demonstrates that considering mRNA and near siRNA binding site features helps improve siRNA design accuracy. The findings may also be helpful in understanding binding efficacy between microRNA and mRNA.

Classification Method of High-Resolution Remote Sensing Scenes Based on Fusion of Global and Local Deep Features

2019 ◽  
Vol 39 (3) ◽  
pp. 0301002
Author(s):  
龚希 Gong Xi ◽  
吴亮 Wu Liang ◽  
谢忠 Xie Zhong ◽  
陈占龙 Chen Zhanlong ◽  
刘袁缘 Liu Yuanyuan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Classification Method ◽  
Global And Local

Facial Expression Recognition Based on Features Fusion

2010 ◽  
Vol 20-23 ◽  
pp. 1253-1259
Author(s):  
Chang Jun Zhou ◽  
Xiao Peng Wei ◽  
Qiang Zhang
Keyword(s):  
Principal Component ◽  
Local Features ◽  
Support Vector ◽  
Expression Recognition ◽  
Global Features ◽  
Features Fusion ◽  
Face Expression ◽  
The Face ◽  
Value Decomposition ◽  
Global And Local

In this paper, we propose a novel algorithm for facial recognition based on features fusion in support vector machine (SVM). First, some local features and global features from pre-processed face images are obtained. The global features are obtained by making use of singular value decomposition (SVD). At the same time, the local features are obtained by utilizing principal component analysis (PCA) to extract the principal Gabor features. Finally, the feature vectors which are fused with global and local features are used to train SVM to realize the face expression recognition, and the computer simulation illustrates the effectivity of this method on the JAFFE database.

Canonical Correlation Analysis on Fusion of Global and Local Features of Face Recognition

2014 ◽  
Vol 926-930 ◽  
pp. 3598-3603
Author(s):  
Xiao Xiong ◽  
Guo Fa Hao ◽  
Peng Zhong
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Local Features ◽  
Global Features ◽  
Features Fusion ◽  
Local Characteristics ◽  
Face Recognition System ◽  
Global And Local ◽  
Effective Integration

Face recognition belongs to the important content of the biometric identification, which is a important method in research of image processing and pattern recognition. It can effectively overcome the traditional authentication defects Through the facial recognition technology. At present, face recognition under ideal state research made some achievements, but the changes in light, shade, expression, posture changes the interference factors such as face recognition is still exist many problems. For this, put forward the integration of global and local features of face recognition research. Practice has proved that through the effective integration of global features and local characteristics, build based on global features and local features fusion face recognition system, can improve the recognition rate of face recognition, face recognition application benefit.

scholarly journals The Influence of Inhibition of Return and Level-Priming on the Global Precedence Effect

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Yan Wu ◽  
Qi Li ◽  
Yuanzi Liu
Keyword(s):  
Inhibition Of Return ◽  
Local Features ◽  
Precedence Effect ◽  
Global Precedence ◽  
Global Features ◽  
Hierarchical Processing ◽  
Inhibition Effect ◽  
Hierarchical Stimuli ◽  
Priming Paradigm ◽  
Global And Local

The aim of the reported experiment was to investigate the effects of inhibition of return (IOR) and level-priming on the global precedence effect (GPE). The classical hierarchical stimuli combined with IOR and the level-priming paradigm were used. The participants selectively attended to the global or local features of compound numerals. The results showed that IOR inhibited the response to the global and local features; moreover, the inhibition effect on the perception of the global features was stronger than that of the local features in the stage of inhibitory processing, resulting in the disappearance of GPE. However, level-priming promoted the response to global and local features, and the promotion effect was stronger on local features, leading to the disappearance of GPE as well. These findings suggested that hierarchical processing was affected by IOR and level-priming, which were correlated with selective attention. Thus, it indicated that global precedence could be involved in attentional mechanisms.

scholarly journals Influence of global and local features on parallel object identification

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 660
Author(s):  
Alvydas Šoliūnas
Keyword(s):  
Response Time ◽  
Visual Information ◽  
Scene Perception ◽  
Local Features ◽  
Object Identification ◽  
Sources Of Information ◽  
Shape Distortion ◽  
Global Features ◽  
Visual Objects ◽  
Global And Local

Background:The present study concerns parallel and serial processing of visual information, or more specifically, whether visual objects are identified successively or simultaneously in multiple object stimulus. Some findings in scene perception demonstrate the potential parallel processing of different sources of information in a stimulus; however, more extensive investigation is needed.Methods:We presented one, two or three visual objects of different categories for 100 ms and afterwards asked subjects whether a specified category was present in the stimulus. We varied the number of objects, the number of categories and the type of object shape distortion (distortion of either global or local features). Results:The response time and accuracy data corresponded to data from a previous experiment, which demonstrated that performance efficiency mostly depends on the number of categories but not on the number of objects. Two and three objects of the same category were identified with the same accuracy and the same response time, but two objects were identified faster and more accurately than three objects if they belonged to different categories. Distortion type did not affect the pattern of performance.Conclusions:The findings suggest the idea that objects of the same category can be identified simultaneously and that identification involves both local and global features.

Recognition of Face Biometrics

2017 ◽  
pp. 440-457
Author(s):  
Pooja Sharma
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Hybrid Approach ◽  
Local Features ◽  
Rotation Invariant ◽  
Global Features ◽  
Local Ternary Patterns ◽  
Common Domain ◽  
Global And Local ◽  
Face Biometrics

In the proposed chapter, a novel, effective, and efficient approach to face recognition is presented. It is a fusion of both global and local features of images, which significantly achieves higher recognition. Initially, the global features of images are determined using polar cosine transforms (PCTs), which exhibit very less computation complexity as compared to other global feature extractors. For local features, the rotation invariant local ternary patterns are used rather than using the existing ones, which help improving the recognition rate and are in alignment with the rotation invariant property of PCTs. The fusion of both acquired global and local features is performed by mapping their features into a common domain. Finally, the proposed hybrid approach provides a robust feature set for face recognition. The experiments are performed on benchmark face databases, representing various expressions of facial images. The results of extensive set of experiments reveal the supremacy of the proposed method over other approaches in terms of efficiency and recognition results.

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