Facial age estimation and gender classification using multi level local phase quantization

2015 ◽  
Author(s):  
Salah Eddine Bekhouche ◽  
Abdelkrim Ouafi ◽  
Azeddine Benlamoudi ◽  
Abdelmalik Taleb-Ahmed ◽  
Abdenour Hadid
Keyword(s):  
Age Estimation ◽  
Gender Classification ◽  
Local Phase ◽  
Local Phase Quantization ◽  
Multi Level ◽  
Phase Quantization ◽  
And Gender ◽  
Facial Age

scholarly journals Gender Classification Based on Multiscale Facial Fusion Feature

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Chunyu Zhang ◽  
Hui Ding ◽  
Yuanyuan Shang ◽  
Zhuhong Shao ◽  
Xiaoyan Fu
Keyword(s):  
Local Binary Pattern ◽  
State Of The Art ◽  
Support Vector ◽  
Gender Classification ◽  
Local Phase ◽  
New Approach ◽  
Local Phase Quantization ◽  
Face Images ◽  
Phase Quantization ◽  
Fusion Feature

For gender classification, we present a new approach based on Multiscale facial fusion feature (MS3F) to classify gender from face images. Fusion feature is extracted by the combination of Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) descriptors, and a multiscale feature is generated through Multiblock (MB) and Multilevel (ML) methods. Support Vector Machine (SVM) is employed as the classifier to conduct gender classification. All the experiments are performed based on the Images of Groups (IoG) dataset. The results demonstrate that the application of Multiscale fusion feature greatly improves the performance of gender classification, and our approach outperforms the state-of-the-art techniques.

scholarly journals Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ji-Yong An ◽  
Fan-Rong Meng ◽  
Zhu-Hong You ◽  
Yu-Hong Fang ◽  
Yu-Jun Zhao ◽  
...  
Keyword(s):  
Protein Sequences ◽  
Relevance Vector Machine ◽  
Experimental Results ◽  
Computational Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Local Phase ◽  
Local Phase Quantization ◽  
Phase Quantization ◽  
Better Than

We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments onYeastandHumandatasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on theYeastdataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

Colored spun fabric texture representation and application by combining spatial features with frequency features

2020 ◽  
pp. 004051752096140
Author(s):  
Li Yuan ◽  
Xue Gong ◽  
Junping Liu ◽  
Yali Yang ◽  
Muli Liu
Keyword(s):  
Classification Accuracy ◽  
Local Binary Pattern ◽  
Learning Algorithm ◽  
Woven Fabric ◽  
Local Phase ◽  
Texture Representation ◽  
Local Phase Quantization ◽  
Fuzzy Transformation ◽  
Representation Model ◽  
Phase Quantization

Colored spun fabrics are difficult to accurately characterize with a local binary pattern due to texture anisotropy caused by the uneven distribution of dyed fibers. In this paper, we present a texture representation model based on spatial and frequency characteristics. The proposed model takes advantage of the local binary pattern and local phase quantization to extract the texture of woven fabric. Then, the two features are connected in series, and the features of dimension reduction by principal component analysis are used to represent the texture of the fabric image. Finally, the hierarchical hybrid classifier is applied to classify the fabric structure. The experimental results show that the local phase quantization feature is robust to the fuzzy transformation and the texture representation model has a stronger ability of texture description than the single local binary pattern feature, with the average classification accuracy of 97.59% on 336 samples. In addition, compared with the deep learning algorithm, the texture representation algorithm can ensure a high classification accuracy.

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