Deep learning has brought significant developments in image understanding tasks such as object detection, image classification, and image segmentation. But the success of image recognition largely relies on supervised learning that requires huge number of human-annotated labels. To avoid costly collection of labeled data and the domains where very few standard pre-trained models exist, self-supervised learning comes to our rescue. Self-supervised learning is a form of unsupervised learning that allows the network to learn rich visual features that help in performing downstream computer vision tasks such as image classification, object detection, and image segmentation. This paper provides a thorough review of self-supervised learning which has the potential to revolutionize the computer vision field using unlabeled data. First, the motivation of self-supervised learning is discussed, and other annotation efficient learning schemes. Then, the general pipeline for supervised learning and self-supervised learning is illustrated. Next, various handcrafted pretext tasks are explained that enable learning of visual features using unlabeled image dataset. The paper also highlights the recent breakthroughs in self-supervised learning using contrastive learning and clustering methods that are outperforming supervised learning. Finally, we have performance comparisons of self-supervised techniques on evaluation tasks such as image classification and detection. In the end, the paper is concluded with practical considerations and open challenges of image recognition tasks in self-supervised learning regime. From the onset of the review paper, the core focus is on visual feature learning from images using the self-supervised approaches.