Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning

The Image classification is one of the preliminary processes, which humans learn as infants. The fundamentals of image classification lie in identifying basic shapes and geometry of objects around us. It is a process which involves the following tasks of pre-processing the image (normalization), image segmentation, extraction of key features and identification of the class. The current image classification techniques are much faster in run time and more accurate than ever before, they can be used for extensive applications including, security features, face recognition for authentication and authorization, traffic identification, medical diagnosis and other fields. The idea of image classification can be solved by different approaches. But the machine learning algorithms are the best among them. These algorithms are based on the idea proposed years ago, but couldn't be implemented due to lack of computational power. With the idea of deep learning, the models are trained better and are able to identify different levels of image representation. The convolutional neural networks revolutionized this field by learning the basic shapes in the first layers and evolving to learn features of the image in the deeper layers, resulting in more accurate image classification. The idea of Convolutional neural network was inspired by the hierarchical representation of neurons by Hubel and Wiesel in 1962, their work was based on the study of stimuli of the visual cortex in cat. It was a fundamental breakthrough in the field of computer vision in understanding the working of visual cortex in humans and animals. In this paper feature of an images is extracted using convolution neural network using the concept of deep learning. Further classification algorithms are implemented for various applications.