scholarly journals Classification of Error-Diffused Halftone Images Based on Spectral Regression Kernel Discriminant Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Zhigao Zeng ◽  
Zhiqiang Wen ◽  
Shengqiu Yi ◽  
Sanyou Zeng ◽  
Yanhui Zhu ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Accuracy Rate ◽  
Kernel Discriminant Analysis ◽  
Image Patches ◽  
Classification Of Error ◽  
Added Benefit ◽  
Feature Dimension ◽  
Spectral Regression ◽  
Halftone Images

This paper proposes a novel algorithm to solve the challenging problem of classifying error-diffused halftone images. We firstly design the class feature matrices, after extracting the image patches according to their statistics characteristics, to classify the error-diffused halftone images. Then, the spectral regression kernel discriminant analysis is used for feature dimension reduction. The error-diffused halftone images are finally classified using an idea similar to the nearest centroids classifier. As demonstrated by the experimental results, our method is fast and can achieve a high classification accuracy rate with an added benefit of robustness in tackling noise.

scholarly journals Classification of Marine Vessels with Multi-Feature Structure Fusion

2019 ◽  
Vol 9 (10) ◽  
pp. 2153 ◽  
Author(s):  
Erhu Zhang ◽  
Kelu Wang ◽  
Guangfeng Lin
Keyword(s):  
Promising Result ◽  
Infrared Image ◽  
Feature Space ◽  
Classification Performance ◽  
Feature Structure ◽  
Redundant Feature ◽  
Feature Dimension ◽  
Spectral Regression ◽  
Marine Vessels

The classification of marine vessels is one of the important problems of maritime traffic. To fully exploit the complementarity between different features and to more effectively identify marine vessels, a novel feature structure fusion method based on spectral regression discriminant analysis (SF-SRDA) was proposed. Firstly, we selected the different convolutional neural network features that better describe the characteristics of ships, and constructed the features based on graphs by the similarity metric. Then we weighed the concatenate multi-feature and fused their structures according to the linear relationship assumption. Finally, we constructed the optimization formula to solve the fusion features and structure by using spectral regression discriminant analyses. Experiments on the VAIS dataset show that the proposed SF-SRDA method can reduce the feature dimension from the original 102,400 dimensions to 5 dimensions, that the classification accuracy of visible images can reach 87.60%, and that that of the infrared image can reach 74.68% at daytime. The experimental results demonstrate that the proposed method can not only extract the optimal features from the original redundant feature space, but also greatly reduce the dimensions of the feature. Furthermore, the classification performance of SF-SRDA also gets a promising result.

scholarly journals Remote hyperspectral sensing for the early detection of Melanaphis sacchari Zehntner, 1897 (Hemiptera: Aphididae) infestations in sorghum leaves

2021 ◽  
pp. 224-232
Author(s):  
Ruth Artemisa Aguilera Hernández ◽  
Manuel Darío Salas Araiza ◽  
Adriana Saldaña Robles ◽  
Alberto Saldaña Robles ◽  
Mónica Trejo Durán ◽  
...  
Keyword(s):  
Abiotic Stress ◽  
Discriminant Analysis ◽  
Spectral Information ◽  
Correct Classification ◽  
Sorghum Vulgare ◽  
Melanaphis Sacchari ◽  
Kernel Discriminant Analysis ◽  
Sugarcane Aphid ◽  
Hyperspectral Sensing

This paper aims to study the reflectance signature information of infested and non-infested sorghum leaves (Sorghum vulgare L.) by sugarcane aphid (Melanaphis sacchari) to discriminate infested sorghum. The study treatments were 0 (0 aphids/leaf), 1 (1-20 aphids/leaf), 2 (21-50 aphids/leaf), 3 (> = 51 aphids/leaf), 4 (> = 51 aphids/leaf + visible damage), 5 (abiotic stress) and 6 (> = 51 aphids/leaf + abiotic stress). An Ocean OpticsTM HR4000 spectrometer was used. The multifactor ANOVA and Kruskal-Wallis tests at 95% confidence indicated that the reflectance at 402.95, 528.43, 658.36, 788.13, and 965.14 nm wavelengths have significant differences between treatments and with the control. Also Kernel Discriminant analysis was carried out and the combination of the wavelengths centered at 788.17 and 965.14 nm allows 70 % of correct classification of treatments. The results indicate that it is possible to detect M. sacchari infested sorghum by using the spectral information of some specific wavelengths. This study may enable the research of an aerial sensor to make recommendation maps of application pesticides.

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