An Exploratory of Hybrid Techniques on Deep Learning for Image Classification

Abstract

Image Mining is an extension of data mining, which is concerned with extracting beneficial information from image data. Images are classified based on texture, size, color and morphology. Neural Networks, ImageNet, VGG16, AlexNet are renowned image recognition techniques used to identify various agriculture, medical, aerial images and so on. Convolution neural network (CNN) is a Machine learning method used to classify the images which are popularly known for robust feature extraction and information mining. A comparative study of seven CNN based hybrid image classification techniques namely CNN-ELM, CNN-KNN, CNN-GA, MLP-CNN, CNN-SVM, CNN-RNN, CNN-LSTM has been done to determine their accuracy.