Research on Recognition Effect of DSCN Network Structure in Hand-Drawn Sketch

With the rapid development of image recognition technology, freehand sketch recognition has attracted more and more attention. How to achieve good recognition effect in the absence of color and texture information is the key to the development of freehand sketch recognition. Traditional nonlearning classical models are highly dependent on manual selection features. To solve this problem, a neural network sketch recognition method based on DSCN structure is proposed in this paper. Firstly, the stroke sequence of the sketch is drawn; then, the feature is extracted according to the stroke sequence combined with neural network, and the extracted image features are used as the input of the model to construct the time relationship between different image features. Through the control experiment on TU-Berlin dataset, the results show that, compared with the traditional nonlearning methods, HOG-SVM, SIFT-Fisher Vector, MKL-SVM, and FV-SP, the recognition accuracy of DSCN network is improved by 15.8%, 10.3%, 6.0%, and 2.9%, respectively. Compared with the classical deep learning model, Alex-Net, the recognition accuracy is improved by 5.6%. The above results show that the DSCN network proposed in this paper has strong ability of feature extraction and nonlinear expression and can effectively improve the recognition accuracy of hand-painted sketches after introducing the stroke order.

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Campus Violence Detection Based on Artificial Intelligent Interpretation of Surveillance Video Sequences

Remote Sensing ◽

10.3390/rs13040628 ◽

2021 ◽

Vol 13 (4) ◽

pp. 628

Author(s):

Liang Ye ◽

Tong Liu ◽

Tian Han ◽

Hany Ferdinando ◽

Tapio Seppänen ◽

...

Keyword(s):

Neural Network ◽

Recognition Accuracy ◽

Role Playing ◽

School Bullying ◽

Image Features ◽

Campus Violence ◽

Surveillance Video ◽

Acoustic Features ◽

Mel Frequency Cepstral Coefficients ◽

Violence Detection

Campus violence is a common social phenomenon all over the world, and is the most harmful type of school bullying events. As artificial intelligence and remote sensing techniques develop, there are several possible methods to detect campus violence, e.g., movement sensor-based methods and video sequence-based methods. Sensors and surveillance cameras are used to detect campus violence. In this paper, the authors use image features and acoustic features for campus violence detection. Campus violence data are gathered by role-playing, and 4096-dimension feature vectors are extracted from every 16 frames of video images. The C3D (Convolutional 3D) neural network is used for feature extraction and classification, and an average recognition accuracy of 92.00% is achieved. Mel-frequency cepstral coefficients (MFCCs) are extracted as acoustic features, and three speech emotion databases are involved. The C3D neural network is used for classification, and the average recognition accuracies are 88.33%, 95.00%, and 91.67%, respectively. To solve the problem of evidence conflict, the authors propose an improved Dempster–Shafer (D–S) algorithm. Compared with existing D–S theory, the improved algorithm increases the recognition accuracy by 10.79%, and the recognition accuracy can ultimately reach 97.00%.

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Sparse Representation Optimization of Gaussian Mixed Feature of Image Based on Convolution Neural Network

10.21203/rs.3.rs-912092/v1 ◽

2021 ◽

Author(s):

Yuguang Ye

Keyword(s):

Neural Network ◽

Image Processing ◽

Sparse Representation ◽

Rapid Development ◽

Vehicle Detection ◽

Response Speed ◽

Gaussian Mixture ◽

Image Features ◽

Convolution Neural Network ◽

Processing Efficiency

Abstract With the rapid development of intelligent algorithm and image processing technology, the limitations of traditional image processing methods are more and more obvious. Based on this, this paper studies a new pattern of sparse representation optimization of image Gaussian mixture feature based on convolution neural network, and designs a sparse representation system model of vehicle detection image based on convolution neural network. The vehicle image data is collected from many aspects, and the convolution neural network is used for comprehensive analysis and evaluation. The model can extract the feature information of the vehicle detection image better by making the scheme of the real-time vehicle detection image and according to the image features and convolution neural network algorithm. The results show that the Gaussian mixture feature sparse representation optimization model based on convolution neural network has the advantages of high feasibility, high data accuracy and high response speed, which can enhance the processing efficiency of vehicle detection image and improve the utilization of local environmental information in the image.

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UAV Remote Sensing Image Automatic Registration Based on Deep Residual Features

Remote Sensing ◽

10.3390/rs13183605 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3605

Author(s):

Xin Luo ◽

Guangling Lai ◽

Xiao Wang ◽

Yuwei Jin ◽

Xixu He ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Rapid Development ◽

Image Features ◽

Automatic Registration ◽

Remote Sensing Images ◽

Registration Accuracy ◽

Registration Method ◽

Uav Images ◽

High Level

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV remote sensing images are increasing sharply. However, due to the limitation of the perspective of UAV remote sensing, the UAV images obtained from different viewpoints of a same scene need to be stitched together for further applications. Therefore, an automatic registration method of UAV remote sensing images based on deep residual features is proposed in this work. It needs no additional training and does not depend on image features, such as points, lines and shapes, or on specific image contents. This registration framework is built as follows: Aimed at the problem that most of traditional registration methods only use low-level features for registration, we adopted deep residual neural network features extracted by an excellent deep neural network, ResNet-50. Then, a tensor product was employed to construct feature description vectors through exacted high-level abstract features. At last, the progressive consistency algorithm (PROSAC) was exploited to remove false matches and fit a geometric transform model so as to enhance registration accuracy. The experimental results for different typical scene images with different resolutions acquired by different UAV image sensors indicate that the improved algorithm can achieve higher registration accuracy than a state-of-the-art deep learning registration algorithm and other popular registration algorithms.

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Monitoring and Recognizing Enterprise Public Opinion from High-Risk Users Based on User Portrait and Random Forest Algorithm

Axioms ◽

10.3390/axioms10020106 ◽

2021 ◽

Vol 10 (2) ◽

pp. 106

Author(s):

Tinggui Chen ◽

Xiaohua Yin ◽

Lijuan Peng ◽

Jingtao Rong ◽

Jianjun Yang ◽

...

Keyword(s):

Neural Network ◽

Public Opinion ◽

High Risk ◽

Random Forest ◽

Recognition Accuracy ◽

Rapid Development ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Random Forest Algorithm ◽

Negative Comments

With the rapid development of “We media” technology, netizens can freely express their opinions regarding enterprise products on a network platform. Consequently, online public opinion about enterprises has become a prominent issue. Negative comments posted by some netizens may trigger negative public opinion, which can have a significant impact on an enterprise’s image. From the perspective of helping enterprises deal with negative public opinion, this paper combines user portrait technology and a random forest algorithm to help enterprises identify high-risk users who have posted negative comments and thus may trigger negative public opinion. In this way, enterprises can monitor the public opinion of high-risk users to prevent negative public opinion events. Firstly, we crawled the information of users participating in discussions of product experience, and we constructed a portrait of enterprise public opinion users. Then, the characteristics of the portraits were quantified into indicators such as the user’s activity, the user’s influence, and the user’s emotional tendency, and the indicators were sorted. According to the order of the indicators, the users were divided into high-risk, moderate-risk, and low-risk categories. Next, a supervised high-risk user identification model for this classification was established, based on a random forest algorithm. In turn, the trained random forest identifier can be used to predict whether the authors of newly published public opinion information are high-risk users. Finally, a back propagation neural network algorithm was used to identify users and compared with the results of model recognition in this paper. The results showed that the average recognition accuracy of the back propagation neural network is only 72.33%, while the average recognition accuracy of the model constructed in this paper is as high as 98.49%, which verifies the feasibility and accuracy of the proposed random forest recognition method.

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A Compact High-Quality Image Demosaicking Neural Network for Edge-Computing Devices

Sensors ◽

10.3390/s21093265 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3265

Author(s):

Shuyu Wang ◽

Mingxin Zhao ◽

Runjiang Dou ◽

Shuangming Yu ◽

Liyuan Liu ◽

...

Keyword(s):

Neural Network ◽

Rapid Development ◽

Receptive Fields ◽

Image Features ◽

Edge Computing ◽

Model Parameters ◽

Image Size ◽

Feature Maps ◽

Gaussian Smoothing ◽

High Quality Image

Image demosaicking has been an essential and challenging problem among the most crucial steps of image processing behind image sensors. Due to the rapid development of intelligent processors based on deep learning, several demosaicking methods based on a convolutional neural network (CNN) have been proposed. However, it is difficult for their networks to run in real-time on edge computing devices with a large number of model parameters. This paper presents a compact demosaicking neural network based on the UNet++ structure. The network inserts densely connected layer blocks and adopts Gaussian smoothing layers instead of down-sampling operations before the backbone network. The densely connected blocks can extract mosaic image features efficiently by utilizing the correlation between feature maps. Furthermore, the block adopts depthwise separable convolutions to reduce the model parameters; the Gaussian smoothing layer can expand the receptive fields without down-sampling image size and discarding image information. The size constraints on the input and output images can also be relaxed, and the quality of demosaicked images is improved. Experiment results show that the proposed network can improve the running speed by 42% compared with the fastest CNN-based method and achieve comparable reconstruction quality as it on four mainstream datasets. Besides, when we carry out the inference processing on the demosaicked images on typical deep CNN networks, Mobilenet v1 and SSD, the accuracy can also achieve 85.83% (top 5) and 75.44% (mAP), which performs comparably to the existing methods. The proposed network has the highest computing efficiency and lowest parameter number through all methods, demonstrating that it is well suitable for applications on modern edge computing devices.

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Image Hashtag Recommendations Using a Voting Deep Neural Network and Associative Rules Mining Approach

Entropy ◽

10.3390/e22121351 ◽

2020 ◽

Vol 22 (12) ◽

pp. 1351

Author(s):

Tomasz Hachaj ◽

Justyna Miazga

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Rapid Development ◽

Classification Problem ◽

Image Features ◽

Source Codes ◽

Confidence Threshold ◽

Social Media Platforms

Hashtag-based image descriptions are a popular approach for labeling images on social media platforms. In practice, images are often described by more than one hashtag. Due the rapid development of deep neural networks specialized in image embedding and classification, it is now possible to generate those descriptions automatically. In this paper we propose a novel Voting Deep Neural Network with Associative Rules Mining (VDNN-ARM) algorithm that can be used to solve multi-label hashtag recommendation problems. VDNN-ARM is a machine learning approach that utilizes an ensemble of deep neural networks to generate image features, which are then classified to potential hashtag sets. Proposed hashtags are then filtered by a voting schema. The remaining hashtags might be included in a final recommended hashtags dataset by application of associative rules mining, which explores dependencies in certain hashtag groups. Our approach is evaluated on a HARRISON benchmark dataset as a multi-label classification problem. The highest values of our evaluation parameters, including precision, recall, and accuracy, have been obtained for VDNN-ARM with a confidence threshold 0.95. VDNN-ARM outperforms state-of-the-art algorithms, including VGG-Object + VGG-Scene precision by 17.91% as well as ensemble–FFNN (intersection) recall by 32.33% and accuracy by 27.00%. Both the dataset and all source codes we implemented for this research are available for download, and our results can be reproduced.

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Research on Methods of English Text Detection and Recognition Based on Neural Network Detection Model

Scientific Programming ◽

10.1155/2021/6406856 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Chunlan Li

Keyword(s):

Neural Network ◽

Recognition Accuracy ◽

Rapid Development ◽

Research Work ◽

Text Detection ◽

English Text ◽

Data Sets ◽

Original Algorithm ◽

Multiple Data Sets ◽

Detection And Recognition

With the rapid development of computer science, a large number of images and an explosive amount of information make it difficult to filter and effectively extract information. This article focuses on the inability of effective detection and recognition of English text content to conduct research, which is useful for improving the application of intelligent analysis significance. This paper studies how to improve the neural network model to improve the efficiency of image text detection and recognition under complex background. The main research work is as follows: (1) An improved CTPN multidirectional text detection algorithm is proposed, and the algorithm is applied to the multidirectional text detection and recognition system. It uses the multiangle rotation of the image to be detected, then fuses the candidate text boxes detected by the CTPN network, and uses the fusion strategy to find the best area of the text. This algorithm solves the problem that the CTPN network can only detect the text in the approximate horizontal direction. (2) An improved CRNN text recognition algorithm is proposed. The algorithm is based on CRNN and combines traditional text features and depth features at the same time, making it possible to recognize occluded text. The algorithm was tested on the IC13 and SVT data sets. Compared with the CRNN algorithm, the recognition accuracy has been improved, and the detection and recognition accuracy has increased by 0.065. This paper verifies the effectiveness of the improved algorithm model on multiple data sets, which can effectively detect various English texts, and greatly improves the detection and recognition performance of the original algorithm.

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Research on Students’ Adaptive Learning System Based on Deep Learning Model

Scientific Programming ◽

10.1155/2021/6593438 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Xi Yang ◽

Zhihan Zhou ◽

Yu Xiao

Keyword(s):

Neural Network ◽

Deep Learning ◽

Adaptive Learning ◽

Rapid Development ◽

Learning Model ◽

Learning System ◽

Learning Resources ◽

Recommendation Algorithm ◽

Adaptive Learning System ◽

Deep Learning Model

With the rapid development of deep learning in recent years, recommendation algorithm combined with deep learning model has become an important direction in the field of recommendation in the future. Personalized learning resource recommendation is the main way to realize students’ adaptation to the learning system. Based on the in-depth learning mode, students’ online learning action data are obtained, and further learning analysis technology is used to construct students’ special learning mode and provide suitable learning resources. The traditional method of introducing learning resources mainly stays at the level of examination questions. What ignores the essence of students’ learning is the learning of knowledge points. Students’ learning process is affected by “before” and “after” learning behavior, which has the characteristics of time. Among them, bidirectional length cyclic neural network is good at considering the “front” and “back” states of recommended nodes when recommending prediction results. For the above situation, this paper proposes a recommendation method of students’ learning resources based on bidirectional long-term and short-term memory cyclic neural network. Firstly, recommend the second examination according to the knowledge points, predict the scores of important steps including the accuracy of the recommended examination of the target students and the knowledge points of the recommended examination, and finally cooperate with the above two prediction results to judge whether the examination questions are finally recommended. Through the comparative experiment with the traditional recommendation algorithm, it is found that the student adaptive learning system based on the deep learning model proposed in this paper has better stability and interpretability in the recommendation results.

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Training Convolutional Neural Network for Sketch Recognition on Large-Scale Dataset

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/1/10 ◽

2019 ◽

pp. 82-89

Author(s):

Wen Zhou ◽

Jinyuan Jia

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Large Scale ◽

Rapid Development ◽

Handheld Devices ◽

Sketch Recognition ◽

Large Scale Dataset ◽

Computer Vision Technology ◽

Recognition Learning ◽

Visual Geometry

With the rapid development of computer vision technology, increasingly more focus has been put on image recognition. More specifically, a sketch is an important hand-drawn image that is garnering increased attention. Moreover, as handheld devices such as tablets, smartphones, etc. have become more popular, it has become increasingly more convenient for people to hand-draw sketches using this equipment. Hence, sketch recognition is a necessary task to improve the performance of intelligent equipment. In this paper, a sketch recognition learning approach is proposed that is based on the Visual Geometry Group16 Convolutional Neural Network (VGG16 CNN). In particular, in order to diminish the effect of the number of sketches on the learning method, we adopt a strategy of increasing the quantity to improve the diversity and scale of sketches. Initially, sketch features are extracted via the pretrained VGG16 CNN. Additionally, we obtain contextual features based on the traverse stroke scheme. Then, the VGG16 CNN is trained using a joint Bayesian method to update the related network parameters. Moreover, this network has been applied to predict the labels of input sketches in order to automatically recognize the label of a sketch. Last but not least, related experiments are conducted, and the comparison of our method with the state-of-the-art methods is performed, which shows that our approach is superior and feasible

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An Unconstrained Face Recognition Method Based on Siamese Networks

10.21203/rs.3.rs-707159/v1 ◽

2021 ◽

Author(s):

Song CunLi ◽

Shouyong Ji

Keyword(s):

Neural Network ◽

Face Recognition ◽

Network Model ◽

High Frequency ◽

Recognition Accuracy ◽

Recognition Rate ◽

Low Frequency ◽

Image Features ◽

Frequency Features ◽

Frequency Feature

Abstract It is aimed at the low accuracy and low efficiency of face recognition under unlimited conditions.In this paper, a Siamese neural Network model SN-LF (Siamese Network based on LBP and Frequency Feature perception) is designed based on the Local Binary Pattern (LBP) and the Frequency sensing model.Based on Siamese neural networks, the network adopts circular LBP algorithm and frequency feature perception to realize face recognition under unrestricted conditions.The LBP algorithm can eliminate the influence of light on the image and provide directional input to the network model at the same time.Frequency feature sensing divides the image features into low frequency features and high frequency features. The low frequency features are compressed in the Siamese neural network to increase the recognition efficiency of the network. At the same time, information is exchanged with the high frequency features, so that the target noise data can be eliminated while the feature data is retained.In this way, the recognition rate of the network is maintained, and the computing speed of the network is improved.Simulation experiments are carried out on standard face dataset CASIA-Webface and Yale-B, and compared with other network models. The experimental results show that the proposed SN-LF network structure can improve the recognition accuracy of the algorithm, and achieve a good recognition accuracy.

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