GNN-CRC: Discriminative Collaborative Representation-Based Classification via Gabor Wavelet Transformation and Nearest Neighbor

2018 ◽  
Vol 23 (5) ◽  
pp. 657-665 ◽  
Author(s):  
Yanghao Zhang ◽  
Shaoning Zeng ◽  
Wei Zeng ◽  
Jianping Gou
Keyword(s):  
Wavelet Transformation ◽  
Nearest Neighbor ◽  
Gabor Wavelet ◽  
Collaborative Representation ◽  
Gabor Wavelet Transformation

A Gabor Wavelet Transformation-Based Texture Images Classification Algorithm

2013 ◽  
Vol 811 ◽  
pp. 430-434
Author(s):  
Hai Feng Wang ◽  
Kun Zhang ◽  
Hong E Ren
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transformation ◽  
Texture Feature ◽  
Near Neighbor ◽  
Classification Algorithm ◽  
Gabor Wavelet ◽  
Accuracy Rate ◽  
Gabor Wavelet Transform ◽  
Texture Image Classification ◽  
Gabor Wavelet Transformation

In this paper, we introduce a texture image classification algorithm based on Gabor wavelet transform. Using Gabor wavelet transform, image is decomposed into sub-bands images in multiresolution and multi-direction, and we extract texture feature from all sub-bands images. Then the algorithm groups feature image into clusters by the k near neighbor algorithm. The experimental results on dataset Brodatz showed that the proposed algorithm can achieve an ideal accuracy rate and excellent classification effect.

FACIAL EXPRESSION RECOGNITION BASED ON GABOR WAVELET TRANSFORMATION AND ELASTIC TEMPLATES MATCHING

2006 ◽  
Vol 06 (01) ◽  
pp. 125-138 ◽  
Author(s):  
YONGZHAO ZHAN ◽  
JINGFU YE ◽  
DEJIAO NIU ◽  
PENG CAO
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Wavelet Transformation ◽  
Recognition Rate ◽  
Image Understanding ◽  
Gabor Wavelet ◽  
Expression Recognition ◽  
K Nearest Neighbors ◽  
Matching Algorithm ◽  
Gabor Wavelet Transformation

Facial expression recognition technology plays an important role in research areas such as psychological studies, image understanding and virtual reality etc. In order to achieve subject-independent facial expression recognition and obtain robustness against illumination variety and image deformation, facial expression recognition methods based on Gabor wavelet transformation and elastic templates matching are presented in this paper. First given a still image containing facial expression information, preprocessors are executed which include gray and scale normalization. Secondly, Gabor wavelet filters are adopted to extract expression features. Then the elastic graph for expression features is constructed. Finally, elastic templates matching algorithm and K-nearest neighbors classifier are used to recognize facial expression. Experiments show that expression features can be extracted effectively by Gabor wavelet transformation, which is insensitive to illumination variety and individual difference, and high recognition rate can be obtained using elastic templates matching algorithm, which is subject-independent.

Personal Identification by Fusing Hand Shape Geometry and Palmprint Features

2013 ◽  
Vol 278-280 ◽  
pp. 1228-1231 ◽  
Author(s):  
Shuang Xu ◽  
Min Li ◽  
Ji Feng Ding ◽  
Yan Qiu Cui
Keyword(s):  
Wavelet Transformation ◽  
Feature Matching ◽  
Recognition Rate ◽  
Extraction Methods ◽  
Personal Identification ◽  
Gabor Wavelet ◽  
Hand Shape ◽  
Identification Approach ◽  
Matching Degree ◽  
Gabor Wavelet Transformation

This paper presents a new personal identification approach with fusion of hand shape geometry and palmprint features based on Gabor wavelet transformation. Two kinds personal features can be extracted form the low-resolution hand images. Hand shape geometry features include length of four fingers and width of palm. The palmprint features are composed of principal lines, wrinkles, minutiae, delta points. In pattern matching, the normalization method of matching degree is proposed. At the same time square difference distance is used to calculate feature matching degree of the hand shape geometry and palmprint. The result of experiment show that this approach is the most suitable, acceptable and the higher recognition rate, respectively, using different feature extraction methods.

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