scholarly journals Rice Disease Image Recognition Based on Improved Multi-scale Stack Autoencoder

2020 ◽  
Vol 13 (1) ◽  
pp. 18
Author(s):  
Jun Meng ◽  
Xingchen Lv ◽  
Lifang Fu ◽  
Qiufeng Wu
Keyword(s):  
Image Recognition ◽  
Extraction Method ◽  
Recognition Accuracy ◽  
Classification Performance ◽  
Identification Performance ◽  
Feature Extraction Method ◽  
Multi Scale ◽  
Stacked Autoencoder ◽  
Rice Disease ◽  
Crop Disease

Recently, deep learning methods are widely used in the rice diseases identification. However, the actual image background of rice disease is complex, the classification performance is not ideal. Therefore, this paper proposed a multi-scale feature extraction method based on stacked autoencoder, named the multi-scale stacked autoencoder (MSSAE), to improve the recognition accuracy of rice diseases. This method extracts the complex rice disease image’s features by two steps. In the first step, the images are preprocessed. Then, the MSSAE extract the multi-scale features through preprocessed rice diseases data in different scales. Through comparative analysis of experiments, the new method achieved greater than 95% precision in the detection of rice diseases. It indicated that the MSSAE model has an outstanding identification performance for actual crop disease image recognition.

Wear Particles Recognition Based on Improved LBP

2013 ◽  
Vol 427-429 ◽  
pp. 1874-1878
Author(s):  
Guo De Wang ◽  
Zhi Sheng Jing ◽  
Guo Wei Qin ◽  
Shan Chao Tu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Binary Sequence ◽  
Texture Feature ◽  
Experimental Results ◽  
Wear Particles ◽  
Texture Feature Extraction ◽  
Feature Extraction Method

Wear particles recognition is a key link in the process of Ferrography analysis. Different kinds of wear particles vary greatly in texture, texture feature is one of the most important feature in wear particles recognition. Local Binary Pattern (LBP) is an efficient operator for texture description. The binary sequence of traditional LBP operator is obtained by the comparison between the gray value of the neighborhood and the gray value of the center pixel of the neighborhood, the comparison is too simple to cause the loss of the texture. In this paper, an improved LBP operator is presented for texture feature extraction and it is applied to the recognition of severe sliding particles, fatigue spall particles and laminar particles. The experimental results show that our method is an effective feature extraction method and obtains better recognition accuracy compared with other methods.

Simulation Research of Feature Extraction Method Based on Nonlinear Manifold Learning

2014 ◽  
Vol 533 ◽  
pp. 247-251
Author(s):  
Hai Bing Xiao ◽  
Xiao Peng Xie
Keyword(s):  
Feature Extraction ◽  
Manifold Learning ◽  
Extraction Method ◽  
Classification Performance ◽  
Locally Linear Embedding ◽  
Simulation Research ◽  
Feature Extraction Method ◽  
Linear Embedding ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

This paper deals with the study of Locally Linear Embedding (LLE) and Hessian LLE nonlinear feature extraction for high dimensional data dimension reduction. LLE and Hessian LLE algorithm which reveals the characteristics of nonlinear manifold learning were analyzed. LLE and Hessian LLE algorithm simulation research was studied through different kinds of sample for dimensionality reduction. LLE and Hessian LLE algorithm’s classification performance was compared in accordance with MDS. The simulation experimental results show that LLE and Hessian LLE are very effective feature extraction method for nonlinear manifold learning.

scholarly journals A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1319
Author(s):  
Haikun Shang ◽  
Junyan Xu ◽  
Yucai Li ◽  
Wei Lin ◽  
Jinjuan Wang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Vibration Signal ◽  
Practical Significance ◽  
Stable Operation ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Multi Scale

Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

scholarly journals Video-Based Sentiment Analysis with hvnLBP-TOP Feature and bi-LSTM

2019 ◽  
Vol 33 ◽  
pp. 9963-9964 ◽  
Author(s):  
Haoran Li ◽  
Hua Xu
Keyword(s):  
Sentiment Analysis ◽  
Extraction Method ◽  
Short Term Memory ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Short Term ◽  
Term Memory ◽  
Feature Extraction Method ◽  
Long Short Term Memory ◽  
New Feature

In this paper, we propose a new feature extraction method called hvnLBP-TOP for video-based sentiment analysis. Furthermore, we use principal component analysis (PCA) and bidirectional long short term memory (bi-LSTM) for dimensionality reduction and classification. We achieved an average recognition accuracy of 71.1% on the MOUD dataset and 63.9% on the CMU-MOSI dataset.

scholarly journals An Image Similarity Invariant Feature Extraction Method Based on Radon Transform

2021 ◽  
Vol 15 ◽  
pp. 288-296
Author(s):  
Hongjun Guo ◽  
Lili Chen
Keyword(s):  
Feature Extraction ◽  
Image Recognition ◽  
Radon Transform ◽  
Extraction Method ◽  
Image Similarity ◽  
Shape Feature ◽  
Feature Extraction Method ◽  
Dimensional Image ◽  
Invariant Feature ◽  
Similarity Invariant

With the advancements of computer technology, image recognition technology has been more and more widely applied and feature extraction is a core problem of image recognition. In study, image recognition classifies the processed image and identifies the category it belongs to. By selecting the feature to be extracted, it measures the necessary parameters and classifies according to the result. For better recognition, it needs to conduct structural analysis and image description of the entire image and enhance image understanding through multi-object structural relationship. The essence of Radon transform is to reconstruct the original N-dimensional image in N-dimensional space according to the N-1 dimensional projection data of N-dimensional image in different directions. The Radon transform of image is to extract the feature in the transform domain and map the image space to the parameter space. This paper study the inverse problem of Radon transform of the upper semicircular curve with compact support and continuous in the support. When the center and radius of a circular curve change in a certain range, the inversion problem is unique when the Radon transform along the upper semicircle curve is known. In order to further improve the robustness and discrimination of the features extracted, given the image translation or proportional scaling and the removal of impact caused by translation and proportion, this paper has proposed an image similarity invariant feature extraction method based on Radon transform, constructed Radon moment invariant and shown the description capacity of shape feature extraction method on shape feature by getting intra-class ratio. The experiment result has shown that the method of this paper has overcome the flaws of cracks, overlapping, fuzziness and fake edges which exist when extracting features alone, it can accurately extract the corners of the digital image and has good robustness to noise. It has effectively improved the accuracy and continuity of complex image feature extraction.

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