Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm

Tian, Youhui

doi:10.1109/access.2020.3006097

Cited by 150 publications

(59 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…is is because image recognition is not an isolated problem, but a basic problem encountered in most topics in the field of pattern recognition. Due to different specific conditions and different solutions, the research on image recognition has important theoretical and practical significance [7]. e problem raised by image recognition is to study the use of computers instead of people to automatically process a large amount of physical information, thereby partially replacing people's mental work.…”

Section: Introductionmentioning

confidence: 99%

Using an Improved PSO-SVM Model to Recognize and Classify the Image Signals

Zhao

Wei

2021

Complexity

View full text Add to dashboard Cite

Image recognition is an important field of artificial intelligence. Its basic idea is to use computers to automatically classify different scenes in the acquired images, instead of traditional manual classification tasks. In this paper, through the analysis of rough set theory and artificial intelligence network, as well as the role of the two in image recognition, the rough set theory and artificial intelligence network are organically combined, and a network based on rough set theory and artificial intelligence network is proposed. Using BP artificial intelligence network model, improved BP artificial intelligence network model, and improved PSO-SVM model to identify and classify the extracted characteristic signals and compare the results, all reached 85% correct rate. The PCA and SVM are combined and applied to the MNIST handwritten digit collection for recognition and classification. At the data level, dimensionality reduction is performed on high-dimensional image data to compress the data. This greatly improves the performance of the algorithm, the recognition accuracy rate is as high as 98%, and the running time is shortened by about 90%. The model first preprocesses the original image data and then uses rough set theory to select features, which reduces the input dimension of the artificial intelligence network, improves the learning and recognition speed of the artificial intelligence network, and further improves the accuracy of recognition. The paper applies the model to handwritten digital image recognition, and the experimental results show that the model is effective and feasible. The system has the characteristics of easy deployment and easy maintenance and integration. Experiments show that the system has good time characteristics in the process of multialgorithm parallel image fusion processing.

show abstract

Section: Introductionmentioning

confidence: 99%

Using an Improved PSO-SVM Model to Recognize and Classify the Image Signals

Zhao

Wei

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…The fully connected layer is responsible for transmitting information to the output layer where the feature maps will lose their spatial topologies, be extended as vectors, and passed through the activation function. As the most excellent and popular neural network model in latest decade, CNN was extensively adopted for various fields, especially image recognition [63,64].…”

Section: Cnn Modelmentioning

confidence: 99%

Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment

Rong

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions.

show abstract

“…To a large extent, it is helpful for solving the problems of time-consuming, laborious, and poor effectiveness that often occur in traditional machine learning methods which need to customize feature extraction rules by themselves. In recent years, with continuous development of deep learning technology, its application value in the fields of image processing [18], pattern recognition [19], and NLP is self-evident. For question classification tasks, deep learning can be used to actively analyze and learn the syntactic and semantic features implicit in the questions.…”

Section: Question Classificationmentioning

confidence: 99%

The Research of Convolutional Neural Network Based on Integrated Classification in Question Classification

Zhen

Sun

2021

Scientific Programming

View full text Add to dashboard Cite

As a new generation of search engine, automatic question answering system (QAS) is becoming more and more important and has become one of the hotspots of computer application research and natural language processing (NLP). However, as an indispensable part of the QAS, the role of question classification is an understood thing in the system. In view of this, to further make the performance of question classification much better, both the feature extraction and the classification model were explored. On the study of existing CNN research, an improved CNN model based on Bagging integrated classification (“W2V + B-CNN” for short) is proposed and applied to question classification. Firstly, we combine the characteristics of short texts, use the Word2Vec tool to map the features of the words to a certain dimension, and organize the question sentences into the form of a two-dimensional matrix similar to the image. Then, the trained word vectors are used as the input of the CNN for feature extraction. Finally, the Bagging integrated classification algorithm is used to replace the Softmax classification of the traditional CNN for classification. In other words, the good of W2V + B-CNN model is that it can make use of the advantages of CNN and Bagging integrated classification at the same time. Overall, the new model can not only use the powerful feature extraction capabilities of CNN to extract the potential features of natural language questions but also use the good data classification capabilities of the integrated classification algorithm for feature classification at the same time, which can help improve the accuracy of the W2V + B-CNN in the application of question classification. The comparative experiment results prove that the effect of the W2V + B-CNN is significantly better than that of the CNN and other classification algorithms in question classification.

show abstract

Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm

Cited by 150 publications

References 44 publications

Using an Improved PSO-SVM Model to Recognize and Classify the Image Signals

Using an Improved PSO-SVM Model to Recognize and Classify the Image Signals

Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment

The Research of Convolutional Neural Network Based on Integrated Classification in Question Classification

Contact Info

Product

Resources

About