  The machine learning community has been overwhelmed by a plethora of deep
learning based approaches. Many challenging computer vision tasks such as
detection, localization, recognition and segmentation of objects in
unconstrained environment are being efficiently addressed by various types of
deep neural networks like convolutional neural networks, recurrent networks,
adversarial networks, autoencoders and so on. While there have been plenty of
analytical studies regarding the object detection or recognition domain, many
new deep learning techniques have surfaced with respect to image segmentation
techniques. This paper approaches these various deep learning techniques of
image segmentation from an analytical perspective. The main goal of this work
is to provide an intuitive understanding of the major techniques that has made
significant contribution to the image segmentation domain. Starting from some
of the traditional image segmentation approaches, the paper progresses
describing the effect deep learning had on the image segmentation domain.
Thereafter, most of the major segmentation algorithms have been logically
categorized with paragraphs dedicated to their unique contribution. With an
ample amount of intuitive explanations, the reader is expected to have an
improved ability to visualize the internal dynamics of these processes.
