Image Classification using Deep Convolutional Neural Networks

Abstract

Image classification is the task of taking an input image and outputting a class (a cat, dog, etc.) or a probability of classes that best describes the image. For humans, this task of recognition is one of the first skills we learn from the moment we are born and is one that comes naturally and effortlessly as adults. Without even thinking twice, we’re able to quickly and seamlessly identify the environment we are in as well as the objects that surround us. When we see an image or just when we look at the world around us, most of the time we are able to immediately characterize the scene and give each object a label, all without even consciously noticing. These skills of being able to quickly recognize patterns, generalize from prior knowledge, and adapt to different image environments are ones that we do not share with our fellow machines. Convolutional neural networks, Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential innovations in the field of computer vision. 2012 was the first year that neural nets grew to prominence as Alex Krizhevsky used them to win that year’s ImageNet competition (basically, the annual Olympics of computer vision), dropping the classification error record from 26% to 15%, an astounding improvement at the time. Ever since then, a host of companies have been using deep learning at the core of their services. In our research we experimentd on image classification using different deep learning frameworks. The following sections describes basics of deep learning and how it can be used in case of image classification and convolutional neural networks. We have also discussed the different deep learning frameworks and current applications. Finally we shared our gained results and knowledge. This will help the researchers to get a clear idea about getting knowledge in the field of image classification with deep convolutional neural networks.