Leaf-based plant species recognition based on improved local binary pattern and extreme learning machine

2019 ◽  
Vol 527 ◽  
pp. 121297 ◽  
Author(s):  
Muammer Turkoglu ◽  
Davut Hanbay
Keyword(s):  
Extreme Learning Machine ◽  
Plant Species ◽  
Local Binary Pattern ◽  
Species Recognition ◽  
Learning Machine

scholarly journals Classificação de Espécies de Plantas Usando Extreme Learning Machine

2019 ◽  
Author(s):  
Larissa Britto ◽  
Luciano Pacífico
Keyword(s):  
Extreme Learning Machine ◽  
Plant Species ◽  
Hypothesis Test ◽  
Species Recognition ◽  
General Purpose ◽  
Statistical Hypothesis ◽  
Data Sets ◽  
Real World Data ◽  
Statistical Hypothesis Test ◽  
Learning Machine

Plants play an important role in nature, but correct plant species identification is still a challenging task for non-specialized people. Many works have been proposed towards the development of automatic plant species recognition systems through Machine Learning methods, but most of them lack the proper experimental analysis. In this work, we evaluate the performance of a general-purpose Artificial Neural Network to perform plant classification task: the Extreme Learning Machine (ELM).We compare ELM with several classifiers from plant recognition literature by means of three real-world data sets obtained from different image processing and feature extraction processes. A statistical hypothesis test is employed to perform proper experimental evaluation.

Combining 2D Gabor and Local Binary Pattern for Facial Expression Recognition Using Extreme Learning Machine

2019 ◽  
Vol 23 (3) ◽  
pp. 444-455 ◽  
Author(s):  
Zhen-Tao Liu ◽  
Si-Han Li ◽  
Wei-Hua Cao ◽  
Dan-Yun Li ◽  
Man Hao ◽  
...  
Keyword(s):  
Facial Expression ◽  
Extreme Learning Machine ◽  
Facial Expression Recognition ◽  
Local Binary Pattern ◽  
Human Robot Interaction ◽  
Support Vector ◽  
Expression Recognition ◽  
Robot Interaction ◽  
Interaction Detection ◽  
Learning Machine

The efficiency of facial expression recognition (FER) is important for human-robot interaction. Detection of the facial region, extraction of discriminative facial expression features, and identification of categories of facial expressions are all related to the recognition accuracy and time-efficiency. An FER framework is proposed, in which 2D Gabor and local binary pattern (LBP) are combined to extract discriminative features of salient facial expression patches, and extreme learning machine (ELM) is adopted to identify facial expression categories. The combination of 2D Gabor and LBP can not only describe multiscale and multidirectional textural features, but also capture small local details. The FER of ELM and support vector machine (SVM) is performed using the Japanese female facial expression database and extended Cohn-Kanade database, respectively, in which both ELM and SVM achieve an accuracy of more than 85%, and the computational efficiency of ELM is higher than that of SVM. The proposed framework has been used in the multimodal emotional communication based humans-robots interaction system, in which FER within 2 seconds enables real-time human-robot interaction.

scholarly journals Classification of Parkinson’s disease patients based on spectrogram using local binary pattern descriptors

2022 ◽  
Vol 2153 (1) ◽  
pp. 012014
Author(s):  
E Gelvez-Almeida ◽  
A Váasquez-Coronel ◽  
R Guatelli ◽  
V Aubin ◽  
M Mora
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Extreme Learning Machine ◽  
Local Binary Pattern ◽  
Audio Signal ◽  
Training Time ◽  
Network Training ◽  
Learning Machine ◽  
Hidden Layer ◽  
Rate Of Success

Abstract Extreme learning machine is an algorithm that has shown a good performance facing classification and regression problems. It has gained great acceptance by the scientific community due to the simplicity of the model and its sola great generalization capacity. This work proposes the use of extreme learning machine neural networks to carry out the classification between Parkinson’s disease patients and healthy individuals. The descriptor used corresponds to the feature vector generated applying the local binary Pattern algorithm to the grayscale spectrograms. The spectrograms are obtained from the audio signal samples from the considered repository. Experiments are conducted with single hidden layer and multilayer extreme learning machine networks comparing the results of each structure. Results show that hierarchical extreme learning machine with three hidden layers has a better general performance over multilayer extreme learning machine networks and a single hidden layer extreme learning machine. The rate of success obtained is within the ranges presented in the literature. However, the hierarchical network training time is considerably faster compared to multilayer networks of three or two hidden layers.

scholarly journals Histogram of Gradient and Local Binary Pattern with Extreme Learning Machine Based Ear Recognition

2019 ◽  
Vol 54 (6) ◽  
Author(s):  
Ahmed Kawther Hussein
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Research Topic ◽  
Learning Models ◽  
Ear Recognition ◽  
Gradient Based ◽  
Learning Machine ◽  
Machine Learning Models

The ear recognition system is an attractive research topic in the area of biometrics. It involves building machine learning models to verify the identities of humans using their ears. In this article, an exploration of the performance of ear recognition using two features - local binary pattern and histogram of gradient - has been done using the famous dataset USTB. The finding is that there is a similarity in the performance of these two features in terms of accuracy with a difference in the number of false predictions. The achieved accuracy of the histogram of gradient based extreme learning machine was 99.86% while for local binary pattern based extreme learning machine it was 99.59%.

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