scholarly journals Hybrid Classification for Face Spoof Detection

2021 ◽  
Vol 9 (11) ◽  
pp. 1732-1739
Author(s):  
Abhishek Mittal
Keyword(s):  
Texture Features ◽  
Ensemble Classifier ◽  
Grey Level ◽  
Training Set ◽  
Query Image ◽  
The Face ◽  
Hybrid Classification ◽  
Ensemble Algorithm ◽  
Occurrence Matrix ◽  
And Training

Abstract: ML (machine learning) is consisted of a method of recognizing face. This technique is useful for the attendance system. Two sets are generated for testing and training phases in order to segment the image, to extract the features and develop a dataset. An image is considered as a testing set; the training set is contrasted when it is essential to identify an image. An ensemble classifier is implemented to classify the test images as recognized or non-recognized. The ensemble algorithm fails to acquire higher accuracy as it classifies the data in two classes. Thus, GLCM (Grey Level Co-occurrence Matrix) is projected for analyzing the texture features in order to detect the face. The attendance of the query image is marked after detecting the face. The simulation outcomes revealed the superiority of the projected technique over the traditional methods concerning accuracy. Keywords: DWT, GLCM, KNN, Decision Tree

scholarly journals Skin Disease Recognition Method Based on Image Color and Texture Features

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Li-sheng Wei ◽  
Quan Gan ◽  
Tao Ji
Keyword(s):  
Skin Disease ◽  
Skin Diseases ◽  
Texture Features ◽  
Support Vector ◽  
Grey Level ◽  
Recognition Method ◽  
Svm Classification ◽  
Color Features ◽  
Automatic Methods ◽  
Occurrence Matrix

Skin diseases have a serious impact on people’s life and health. Current research proposes an efficient approach to identify singular type of skin diseases. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases. In this paper, three type skin diseases such as herpes, dermatitis, and psoriasis skin disease could be identified by a new recognition method. Initially, skin images were preprocessed to remove noise and irrelevant background by filtering and transformation. Then the method of grey-level co-occurrence matrix (GLCM) was introduced to segment images of skin disease. The texture and color features of different skin disease images could be obtained accurately. Finally, by using the support vector machine (SVM) classification method, three types of skin diseases were identified. The experimental results demonstrate the effectiveness and feasibility of the proposed method.

scholarly journals Multi Object and Dynamic Query Based CBIR System using DCT Incorporated with HOG and HTF

2019 ◽  
Vol 9 (2S3) ◽  
pp. 590-592
Keyword(s):  
Retrieval System ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Texture Features ◽  
Gray Level ◽  
Query Image ◽  
User Expectations ◽  
Multi Level ◽  
Occurrence Matrix ◽  
Dynamic Query

This work contributes multi object detection and dynamic query image based retrieval system. Generally, finding relevance and matching user expectations is very critical based on query key information and these results irrelevant responses which will produce low similarity index. Consequently, CBIR system took a major responsibility of identifying new objects, retrieving similar objects or contents based on multi query and dynamic keywords with improved recall and precision as per requirement of the users. At this juncture, Discrete Curvelet Transform with the incorporation of HOG and HTF based approach is proposed to handle commercial image, medical images and types of multi model images. This proposed approach mainly focuses on extracting scaled features for finding correlation among the query and database images. To start with the process, query image is decomposed into multi level sub images to extract set of texture features at two levels. These features are estimated by Gray Level Co-occurrence Matrix (GLCM) and HOG descriptor based techniques is adapted to find scaled vectors with reduced dimensionality. This method outperform compared as compared to existing method is authenticated from experimental results.

scholarly journals Multi Object and Dynamic Query Based CBIR System using DCT Incorporated with HOG and HTF

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1358-1369
Keyword(s):  
Retrieval System ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Texture Features ◽  
Gray Level ◽  
Query Image ◽  
User Expectations ◽  
Multi Level ◽  
Occurrence Matrix ◽  
Dynamic Query

This work contributes multi object detection and dynamic query image based retrieval system. Generally, finding relevance and matching user expectations is very critical based on query key information and these results irrelevant responses which will produce low similarity index. Consequently, CBIR system took a major responsibility of identifying new objects, retrieving similar objects or contents based on multi query and dynamic keywords with improved recall and precision as per requirement of the users. At this juncture, Discrete Curvelet Transform with the incorporation of HOG and HTF based approach is proposed to handle commercial image, medical images and types of multi model images. This proposed approach mainly focuses on extracting scaled features for finding correlation among the query and database images. To start with the process, query image is decomposed into multi level sub images to extract set of texture features at two levels. These features are estimated by Gray Level Co-occurrence Matrix (GLCM) and HOG descriptor based techniques is adapted to find scaled vectors with reduced dimensionality. This method outperform compared as compared to existing method is authenticated from experimental results.

Extract the Similar Images Using the Grey Level Co-Occurrence Matrix and the Hu Invariants Moments

2020 ◽  
Vol 38 (5A) ◽  
pp. 719-727
Author(s):  
Beshaier A. Abdulla ◽  
Yossra H. Ali ◽  
Nuha J. Ibrahim
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Distance Measure ◽  
Similarity Index ◽  
Structural Similarity ◽  
Grey Level ◽  
Query Image ◽  
Image Retrieval System ◽  
Similar Images ◽  
Occurrence Matrix

In the last years, many types of research have introduced different methods and techniques for a correct and reliable image retrieval system. The goal of this paper is a comparison study between two different methods which are the Grey level co-occurrence matrix and the Hu invariants moments, and this study is done by building up an image retrieval system employing each method separately and comparing between the results. The Euclidian distance measure is used to compute the similarity between the query image and database images. Both systems are evaluated according to the measures that are used in detection, description, and matching fields which are precision, recall, and accuracy, and addition to that mean square error (MSE) and structural similarity index (SSIM) is used.  And as it shows from the results the Grey level co-occurrence matrix (GLCM) had outstanding and better results from the Hu invariants moment method.

scholarly journals Contrast-Enhanced MRI Texture Parameters as Potential Prognostic Factors for Primary Central Nervous System Lymphoma Patients Receiving High-Dose Methotrexate-Based Chemotherapy

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Chaoyue Chen ◽  
Hongyu Zhuo ◽  
Xiawei Wei ◽  
Xuelei Ma
Keyword(s):  
Magnetic Resonance Imaging ◽  
Cox Regression ◽  
Central Nervous System Lymphoma ◽  
Texture Features ◽  
Regression Analyses ◽  
Grey Level ◽  
Resonance Imaging ◽  
Contrast Enhanced ◽  
Magnetic Resonance Imaging Mri ◽  
Occurrence Matrix

Introduction. The purpose of this study was to evaluate the prognostic value of texture features on contrast-enhanced magnetic resonance imaging (MRI) for patients with primary central nervous system lymphoma (PCNSL). Methods. In this retrospective study, fifty-two patients diagnosed with PCNSL were enrolled from October 2010 to March 2017. The texture feature of tumor tissue on the histogram-based matrix (histo-) and the grey-level co-occurrence matrix (GLCM) was retrieved by contrast-enhanced T1-weighted imaging before any antitumor treatment. Receiver operating characteristic curve analyses were performed to obtain their optimal cutoff values, based on which we dichotomized patients into subgroups. The Kaplan–Meier analyses were conducted to compare overall survival (OS) of subgroups, and multivariate Cox regression analyses were used to determine if they could be taken as independent prognostic factors. Results. Ten texture features were extracted from the MR image, including Energy, Entropy, Kurtosis, Skewness on the histogram-based matrix, and Correlation, Contrast, Dissimilarity, Energy, Entropy, and Homogeneity on the grey-level co-occurrence matrix. Three of them (GLCM-Contrast, GLCM-Dissimilarity, and GLCM-Homogeneity) are shown to be significant in relation to overall survival (OS). The multivariate Cox regression analyses suggest that GLCM-Homogeneity could be taken as independent predictors. Conclusions. The texture features of contrast-enhanced magnetic resonance imaging (MRI) could potentially serve as prognostic biomarkers for PCNSL patients.

scholarly journals Multi-scale characterizations of colon polyps via computed tomographic colonography

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Weiguo Cao ◽  
Marc J. Pomeroy ◽  
Yongfeng Gao ◽  
Matthew A. Barish ◽  
Almas F. Abbasi ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Texture Features ◽  
Learning Model ◽  
Colon Polyps ◽  
Texture Descriptor ◽  
Operating Characteristics ◽  
Computed Tomographic ◽  
Multi Scale ◽  
Definition Of ◽  
Occurrence Matrix

AbstractTexture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

Nipple Localization in Automated Whole Breast Ultrasound Coronal Scans Using Ensemble Learning

2020 ◽  
Vol 43 (1) ◽  
pp. 29-45
Author(s):  
Alex Noel Joseph Raj ◽  
Ruban Nersisson ◽  
Vijayalakshmi G. V. Mahesh ◽  
Zhemin Zhuang
Keyword(s):  
Texture Features ◽  
Breast Ultrasound ◽  
Support Vector ◽  
Ultrasound Images ◽  
Shadow Area ◽  
Hu Moments ◽  
Artificial Neural Network Ann ◽  
Cade System ◽  
Occurrence Matrix ◽  
Shape Complexity

Nipple is a vital landmark in the breast lesion diagnosis. Although there are advanced computer-aided detection (CADe) systems for nipple detection in breast mediolateral oblique (MLO) views of mammogram images, few academic works address the coronal views of breast ultrasound (BUS) images. This paper addresses a novel CADe system to locate the Nipple Shadow Area (NSA) in ultrasound images. Here the Hu Moments and Gray-level Co-occurrence Matrix (GLCM) were calculated through an iterative sliding window for the extraction of shape and texture features. These features are then concatenated and fed into an Artificial Neural Network (ANN) to obtain probable NSA’s. Later, contour features, such as shape complexity through fractal dimension, edge distance from the periphery and contour area, were computed and passed into a Support Vector Machine (SVM) to identify the accurate NSA in each case. The coronal plane BUS dataset is built upon our own, which consists of 64 images from 13 patients. The test results show that the proposed CADe system achieves 91.99% accuracy, 97.55% specificity, 82.46% sensitivity and 88% F-score on our dataset.

An Image Retrieval Algorithm Base on Texture Features

2014 ◽  
Vol 668-669 ◽  
pp. 1041-1044
Author(s):  
Lin Lin Song ◽  
Qing Hu Wang ◽  
Zhi Li Pei
Keyword(s):  
Image Retrieval ◽  
Statistical Methods ◽  
Texture Features ◽  
Retrieval Algorithm ◽  
Occurrence Matrix ◽  
Algorithm Base

This paper firstly studies the texture features. We construct a gray-difference primitive co-occurrence matrix to extract texture features by combining statistical methods with structural ones. The experiment results show that the features of the gray-difference primitive co-occurrence matrix are more delicate than the traditional gray co-occurrence matrix.

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