Feature extraction technique based on Hopfield neural network and joint transform correlation

2004 ◽  
Author(s):  
Abdullah Bal ◽  
Mohammad S. Alam
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Hopfield Neural Network ◽  
Extraction Technique ◽  
Joint Transform Correlation ◽  
Feature Extraction Technique

scholarly journals PLANT SPECIE CLASSIFICATION USING SINUOSITY COEFFICIENTS OF LEAVES

2018 ◽  
Vol 37 (2) ◽  
pp. 119 ◽  
Author(s):  
Jules R Kala ◽  
Serestina Viriri
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Basis Function ◽  
Leaf Shape ◽  
Shape Descriptor ◽  
Automated System ◽  
Extraction Technique ◽  
Accuracy Rate ◽  
Geometrical Features ◽  
Feature Extraction Technique

Forests are the lungs of our planet. Conserving the plants may require the development of an automated system that will identify plants using leaf features such as shape, color, and texture. In this paper, a leaf shape descriptor based on sinuosity coefficients is proposed. The sinuosity coefficients are defined using the sinuosity measure, which is a measure expressing the degree of meandering of a curve. The initial empirical experiments performed on the LeafSnap dataset on the usage of four sinuosity coefficients to characterize the leaf images using the Radial Basis Function Neural Network (RBF) and Multilayer Perceptron (MLP) classifiers achieved accurate classification rates of 88% and 65%, respectively. The proposed feature extraction technique is further enhanced through the addition of leaf geometrical features, and the accurate classification rates of 93% and 82% were achieved using RBF and MLP, respectively. The overall results achieved showed that the proposed feature extraction technique based on the sinuosity coefficients of leaves, complemented with geometrical features improve the accuracy rate of plant classification using leaf recognition.

scholarly journals Music Genre Classification using Spectral Analysis Techniques With Hybrid Convolution-Recurrent Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 149-154
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Recurrent Neural Network ◽  
Performance Measure ◽  
Extraction Technique ◽  
Short Term ◽  
Analysis Techniques ◽  
Genre Classification ◽  
Audio Data ◽  
Feature Extraction Technique

In this work, the objective is to classify the audio data into specific genres from GTZAN dataset which contain about 10 genres. First, it perform the audio splitting to make it signal into clips which contains homogeneous content. Short-term Fourier Transform (STFT), Mel-spectrogram and Mel-frequency cepstrum coefficient (MFCC) are the most common feature extraction technique and each feature extraction technique has been successful in their own various audio applications. Then, these feature extractions of the audio fed to the Convolution Neural Network (CNN) model and VGG16 Neural Network model, which consist of 16 convolution layers network. We perform different feature extraction with different CNN and VGG16 model with or without different Recurrent Neural Network (RNN) and evaluated performance measure. In this model, it has achieved overall accuracy 95.5\% for this task.

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