A Novel Feature Extraction System for Cursive Word Vocabulary Recognition using Local Features Descriptors and Gabor Filter

2020 ◽  
Author(s):  
Soufiane Hamida ◽  
Bouchaib Cherradi ◽  
Oumaima Terrada ◽  
Abdelhadi Raihani ◽  
Hassan Ouajji ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Gabor Filter ◽  
Local Features ◽  
Extraction System ◽  
Vocabulary Recognition

scholarly journals Relative Total Variation Structure Analysis-Based Fusion Method for Hyperspectral and LiDAR Data Classification

2021 ◽  
Vol 13 (6) ◽  
pp. 1143
Author(s):  
Yinghui Quan ◽  
Yingping Tong ◽  
Wei Feng ◽  
Gabriel Dauphin ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Total Variation ◽  
Structure Analysis ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Heterogeneous Data ◽  
Lidar Data ◽  
Fusion Method ◽  
Wide Range ◽  
Relative Total Variation

The fusion of the hyperspectral image (HSI) and the light detecting and ranging (LiDAR) data has a wide range of applications. This paper proposes a novel feature fusion method for urban area classification, namely the relative total variation structure analysis (RTVSA), to combine various features derived from HSI and LiDAR data. In the feature extraction stage, a variety of high-performance methods including the extended multi-attribute profile, Gabor filter, and local binary pattern are used to extract the features of the input data. The relative total variation is then applied to remove useless texture information of the processed data. Finally, nonparametric weighted feature extraction is adopted to reduce the dimensions. Random forest and convolutional neural networks are utilized to evaluate the fusion images. Experiments conducted on two urban Houston University datasets (including Houston 2012 and the training portion of Houston 2017) demonstrate that the proposed method can extract the structural correlation from heterogeneous data, withstand a noise well, and improve the land cover classification accuracy.

scholarly journals Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation

2021 ◽  
Vol 13 (22) ◽  
pp. 4518
Author(s):  
Xin Zhao ◽  
Jiayi Guo ◽  
Yueting Zhang ◽  
Yirong Wu
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Semantic Segmentation ◽  
Local Features ◽  
Local Feature ◽  
Global Information ◽  
Deep Convolutional Neural Networks ◽  
Global Representation ◽  
Local Feature Extraction ◽  
Global And Local

The semantic segmentation of remote sensing images requires distinguishing local regions of different classes and exploiting a uniform global representation of the same-class instances. Such requirements make it necessary for the segmentation methods to extract discriminative local features between different classes and to explore representative features for all instances of a given class. While common deep convolutional neural networks (DCNNs) can effectively focus on local features, they are limited by their receptive field to obtain consistent global information. In this paper, we propose a memory-augmented transformer (MAT) to effectively model both the local and global information. The feature extraction pipeline of the MAT is split into a memory-based global relationship guidance module and a local feature extraction module. The local feature extraction module mainly consists of a transformer, which is used to extract features from the input images. The global relationship guidance module maintains a memory bank for the consistent encoding of the global information. Global guidance is performed by memory interaction. Bidirectional information flow between the global and local branches is conducted by a memory-query module, as well as a memory-update module, respectively. Experiment results on the ISPRS Potsdam and ISPRS Vaihingen datasets demonstrated that our method can perform competitively with state-of-the-art methods.

scholarly journals Pattern Recognition by Hierarchical Feature Extraction

2003 ◽  
Vol 15 (3) ◽  
pp. 278-285
Author(s):  
Daigo Misaki ◽  
◽  
Shigeru Aomura ◽  
Noriyuki Aoyama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Input Data ◽  
Local Features ◽  
Detailed Classification

We discuss effective pattern recognition for contour images by hierarchical feature extraction. When pattern recognition is done for an unlimited object, it is effective to see the object in a perspective manner at the beginning and next to see in detail. General features are used for rough classification and local features are used for a more detailed classification. D-P matching is applied for classification of a typical contour image of individual class, which contains selected points called ""landmark""s, and rough classification is done. Features between these landmarks are analyzed and used as input data of neural networks for more detailed classification. We apply this to an illustrated referenced book of insects in which much information is classified hierarchically to verify the proposed method. By introducing landmarks, a neural network can be used effectively for pattern recognition of contour images.

scholarly journals A cluster-based feature selection method for image texture classification

2019 ◽  
Vol 14 (3) ◽  
pp. 1433 ◽  
Author(s):  
Abbas F. H. Alharan ◽  
Hayder K. Fatlawi ◽  
Nabeel Salih Ali
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Clustering Algorithm ◽  
Gabor Filter ◽  
Texture Feature ◽  
Image Feature ◽  
Image Texture ◽  
Support Vector ◽  
Texture Image ◽  
Texture Feature Extraction

<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>

scholarly journals Research and Development of Palmprint Authentication System Based on Android Smartphones

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xinman Zhang ◽  
Kunlei Jing ◽  
Guokun Song
Keyword(s):  
Feature Extraction ◽  
Real World ◽  
Gabor Filter ◽  
Identity Authentication ◽  
Discriminative Feature ◽  
Authentication System ◽  
The One ◽  
User Friendly ◽  
The Impact ◽  
Improved Algorithm

The security problems of online transactions by smartphones reveal extreme demand for reliable identity authentication systems. With a lower risk of forgery, richer texture, and more comfortable acquisition mode, compared with face, fingerprint, and iris, palmprint is rarely adopted for identity authentication. In this paper, we develop an effective and full-function palmprint authentication system regarding the application on an Android smartphone, which bridges the algorithmic study and application of palmprint authentication. In more detail, an overall system framework is designed with complete functions, including palmprint acquisition, key points location, ROI segmentation, feature extraction, and feature coding. Basically, we develop a palmprint authentication system having user-friendly interfaces and good compatibility with the Android smartphone. Particularly, on the one hand, to guarantee the effectiveness and efficiency of the system, we exploit the practical Log-Gabor filter for feature extraction and discuss the impact of filtering direction, downsampling ratio, and discriminative feature coding to propose an improved algorithm. On the other hand, after exploring the hardware components of the smartphone and the technical development of the Android system, we provide an open technology to extend the biometric methods to real-world applications. On the public PolyU databases, simulation results suggest that the improved algorithm outperforms the original one with a promising accuracy of 100% and a good speed of 0.041 seconds. In real-world authentication, the developed system achieves an accuracy of 98.40% and a speed of 0.051 seconds. All the results verify the accuracy and timeliness of the developed system.

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