EEG Signals Classification in Time-Frequency Images by Fusing Rotation-Invariant Local Binary Pattern and Gray Level Co-occurrence Matrix Features

2021 ◽  
pp. 347-358
Author(s):  
Zhongyi Hu ◽  
Zhenzhen Luo ◽  
Shan Jin ◽  
Zuoyong Li
Keyword(s):  
Local Binary Pattern ◽  
Gray Level ◽  
Eeg Signals ◽  
Rotation Invariant ◽  
Time Frequency ◽  
Occurrence Matrix

scholarly journals LBP and GLCM Based Image Forgery Recognition

2018 ◽  
Vol 7 (4.6) ◽  
pp. 217
Author(s):  
D. Vaishnavi ◽  
T. S. Subashini ◽  
G. N. Balaji ◽  
D. Mahalakshmi
Keyword(s):  
Neural Network ◽  
Local Binary Pattern ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Gray Level ◽  
Image Splicing ◽  
Naked Eye ◽  
Occurrence Matrix

The forgery of digital images became very easy and it’s very difficult to ascertain the authenticity of such images by naked eye. Among the various kinds of image forgeries, image splicing is a frequent and widely used technique. Even though various methods are available to detect image splicing forgery, authors have attempted to provide a novel hybrid method which can yield greater accuracy, sensitivity and specificity. In this method, gray level co-occurrence matrix (GLCM) features are extracted using local binary pattern (LBP) operator on the image and the detection of the splicing forged images among the authentic images is done using the popular pattern recognition algorithms such as combined k-NN (Comb-KNN), back propagation neural network (BPNN) and support vector machine (SVM). The recorded results are also compared with the existing results of the previous studies to ascertain the quality of the results.  

Machinery fault diagnosis based on time–frequency images and label consistent K-SVD

2017 ◽  
Vol 232 (7) ◽  
pp. 1317-1330
Author(s):  
HD Yuan ◽  
J Chen ◽  
GM Dong
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Image Classification ◽  
Mechanical Vibration ◽  
Gray Level ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Occurrence Matrix ◽  
Machinery Fault Diagnosis

Wavelet time–frequency analysis has been widely used for machinery fault diagnosis. Mechanical vibration signals can be converted to time–frequency images using wavelet transform, so machinery fault diagnosis can be transformed to the problem of image classification. Label consistent K-SVD algorithm has been proven to be effective in image classification, which incorporates a label consistent term namely discriminative sparse code error into the objective function. Therefore, in this paper, a novel bearing fault diagnosis method based on wavelet time–frequency image and label consistent K-SVD is proposed. Firstly, continuous wavelet transform is utilized to generate wavelet time–frequency images that can fully reflect bearing fault characteristics. Then texture feature extraction based on gray level co-occurrence matrix is implemented on the wavelet time–frequency images. Finally, label consistent K-SVD is conducted for classification of the time–frequency images, and thus bearing fault diagnosis is realized. The experiment results show that the texture features based on gray level co-occurrence matrix of wavelet time–frequency images can effectively extract the fault characteristics of rolling bearings, and label consistent K-SVD performs better than other classification methods based on dictionary learning under the same parameters.

scholarly journals Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features

2021 ◽  
Vol 11 ◽  
Author(s):  
Yan Hao ◽  
Shichang Qiao ◽  
Li Zhang ◽  
Ting Xu ◽  
Yanping Bai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Pathological Examination ◽  
Support Vector ◽  
Gray Level ◽  
Histopathological Images ◽  
Occurrence Matrix ◽  
Low Dimensional ◽  
Images Recognition

Breast cancer (BC) is the primary threat to women’s health, and early diagnosis of breast cancer is imperative. Although there are many ways to diagnose breast cancer, the gold standard is still pathological examination. In this paper, a low dimensional three-channel features based breast cancer histopathological images recognition method is proposed to achieve fast and accurate breast cancer benign and malignant recognition. Three-channel features of 10 descriptors were extracted, which are gray level co-occurrence matrix on one direction (GLCM1), gray level co-occurrence matrix on four directions (GLCM4), average pixel value of each channel (APVEC), Hu invariant moment (HIM), wavelet features, Tamura, completed local binary pattern (CLBP), local binary pattern (LBP), Gabor, histogram of oriented gradient (Hog), respectively. Then support vector machine (SVM) was used to assess their performance. Experiments on BreaKHis dataset show that GLCM1, GLCM4 and APVEC achieved the recognition accuracy of 90.2%-94.97% at the image level and 89.18%-94.24% at the patient level, which is better than many state-of-the-art methods, including many deep learning frameworks. The experimental results show that the breast cancer recognition based on high dimensional features will increase the recognition time, but the recognition accuracy is not greatly improved. Three-channel features will enhance the recognizability of the image, so as to achieve higher recognition accuracy than gray-level features.

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