Face Image Feature Extraction based on Deep Learning Algorithm

In this paper, a novel image projection analysis method (UIPDA) is first developed for image feature extraction. In contrast to Liu's projection discriminant method, UIPDA has the desirable property that the projected feature vectors are mutually uncorrelated. Also, a new LDA technique called EULDA is presented for further feature extraction. The proposed methods are tested on the ORL and the NUST603 face databases. The experimental results demonstrate that: (i) UIPDA is superior to Liu's projection discriminant method and more efficient than Eigenfaces and Fisherfaces; (ii) EULDA outperforms the existing PCA plus LDA strategy; (iii) UIPDA plus EULDA is a very effective two-stage strategy for image feature extraction.

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Using Image Feature Extraction to Identification of Ancient Ceramics Based on Partial Differential Equation

Advances in Mathematical Physics ◽

10.1155/2022/3276776 ◽

2022 ◽

Vol 2022 ◽

pp. 1-12

Author(s):

Chuanbao Niu ◽

Mingzhu Zhang

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Extraction Method ◽

Recognition System ◽

Image Features ◽

Image Feature ◽

Image Feature Extraction ◽

Ancient Ceramics ◽

Ceramic Vessel ◽

Ancient Ceramic

This paper presents an in-depth study and analysis of the image feature extraction technique for ancient ceramic identification using an algorithm of partial differential equations. Image features of ancient ceramics are closely related to specific raw material selection and process technology, and complete acquisition of image features of ancient ceramics is a prerequisite for achieving image feature identification of ancient ceramics, since the quality of extracted area-grown ancient ceramic image feature extraction method is closely related to the background pixels and does not have generalizability. In this paper, we propose a deep learning-based extraction method, using Eased as a deep learning support platform, to extract and validate 5834 images of 272 types of ancient ceramics from kilns, celadon, and Yue kilns after manual labelling and training learning, and the results show that the average complete extraction rate is higher than 99%. The implementation of the deep learning method is summarized and compared with the traditional region growth extraction method, and the results show that the method is robust with the increase of the learning amount and has generalizability, which is a new method to effectively achieve the complete image feature extraction of ancient ceramics. The main content of the finite difference method is to use the ratio of the difference between the function values of two adjacent points and the distance between the two points to approximate the partial derivative of the function with respect to the variable. This idea was used to turn the problem of division into a problem of difference. Recognition of ancient ceramic image features was realized based on the extraction of the overall image features of ancient ceramics, the extraction and recognition of vessel type features, the quantitative recognition of multidimensional feature fusion ornamentation image features, and the implementation of deep learning based on inscription model recognition image feature classification recognition method; three-layer B/S architecture web application system and cross-platform system language called as the architectural support; and database services, deep learning packaging, and digital image processing. The specific implementation method is based on database service, deep learning encapsulation, digital image processing, and third-party invocation, and the service layer fusion and relearning mechanism is proposed to achieve the preliminary intelligent recognition system of ancient ceramic vessel type and ornament image features. The results of the validation test meet the expectation and verify the effectiveness of the ancient ceramic vessel type and ornament image feature recognition system.

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Deep learning for constructing microblog behavior representation to identify social media user's personality

10.7287/peerj.preprints.1906 ◽

2016 ◽

Author(s):

Xiaoqian Liu ◽

Tingshao Zhu

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Prediction Model ◽

Learning Algorithm ◽

Rapid Development ◽

Feature Learning ◽

Extraction Methods ◽

Deep Learning Algorithm ◽

Personality Prediction ◽

Behavior Description

Due to the rapid development of information technology, Internet has become part of everyday life gradually. People would like to communicate with friends to share their opinions on social networks. The diverse social network behavior is an ideal users' personality traits reflection. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic. Although they work fairly well, but it is hard to extend and maintain. In this paper, for personality prediction, we utilize deep learning algorithm to build feature learning model, which could unsupervised extract Linguistic Representation Feature Vector (LRFV) from text published on Sina Micro-blog actively. Compared with other feature extraction methods, LRFV, as an abstract representation of Micro-blog content, could describe use's semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of prediction model respectively. The results show that LRFV performs more excellently in micro-blog behavior description and improve the performance of personality prediction model.

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Scene Matching Method for Children’s Psychological Distress Based on Deep Learning Algorithm

Complexity ◽

10.1155/2021/6638522 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Junli Su

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Psychological Distress ◽

Learning Algorithm ◽

Matching Method ◽

Scene Matching ◽

Deep Learning Algorithm ◽

Person Perspective ◽

Huge Data ◽

Study Results

In the process of children’s psychological development, various levels of psychological distress often occur, such as attention problems, emotional problems, adaptation problems, language problems, and motor coordination problems; these problems have seriously affected children’s healthy growth. Scene matching in the treatment of psychological distress can prompt children to change from a third-person perspective to a first-person perspective and shorten the distance between scene contents and child’s perceptual experience. As a part of machine learning, deep learning can perform mapping transformations in huge data, process huge data with the help of complex models, and extract multilayer features of scene information. Based on the summary and analysis of previous research works, this paper expounded the research status and significance of the scene matching method for children’s psychological distress, elaborated the development background, current status, and future challenges of deep learning algorithm, introduced the methods and principles of depth spatiotemporal feature extraction algorithm and dynamic scene understanding algorithm, constructed a scene matching model for children’s psychological distress based on deep learning algorithm, analyzed the scene feature extraction and matching function construction of children’s psychological distress, proposed a scene matching method for children’s psychological distress based on deep learning algorithm, performed scene feature matching and information processing of children’s psychological distress, and finally conduced a simulation experiment and analyzed its results. The results show that the deep learning algorithm can have a deep and abstract mining on the characteristics of children’s psychological distress scenes and obtain a large amount of more representative characteristic information through training on large-scale data, thereby improving the accuracy of classification and matching of children’s psychological distress scenes. The study results of this paper provide a reference for further researches on the scene matching method for children’s psychological distress based on deep learning algorithm.

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Deep learning for constructing microblog behavior representation to identify social media user's personality

10.7287/peerj.preprints.1906v1 ◽

2016 ◽

Author(s):

Xiaoqian Liu ◽

Tingshao Zhu

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Prediction Model ◽

Learning Algorithm ◽

Rapid Development ◽

Feature Learning ◽

Extraction Methods ◽

Deep Learning Algorithm ◽

Personality Prediction ◽

Behavior Description

Due to the rapid development of information technology, Internet has become part of everyday life gradually. People would like to communicate with friends to share their opinions on social networks. The diverse social network behavior is an ideal users' personality traits reflection. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic. Although they work fairly well, but it is hard to extend and maintain. In this paper, for personality prediction, we utilize deep learning algorithm to build feature learning model, which could unsupervised extract Linguistic Representation Feature Vector (LRFV) from text published on Sina Micro-blog actively. Compared with other feature extraction methods, LRFV, as an abstract representation of Micro-blog content, could describe use's semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of prediction model respectively. The results show that LRFV performs more excellently in micro-blog behavior description and improve the performance of personality prediction model.

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