# Understanding Deep Learning Techniques for Image Segmentation

## TL;DR

This paper provides an analytical overview of deep learning techniques for image segmentation, highlighting their evolution, categorization, and impact on the field to enhance understanding of their internal mechanisms.

## Contribution

It offers a comprehensive, intuitive analysis of major deep learning-based image segmentation methods, emphasizing their unique contributions and evolution from traditional approaches.

## Key findings

- Deep learning significantly advanced image segmentation techniques.
- Major algorithms are logically categorized with explanations of their unique contributions.
- The paper enhances understanding of the internal dynamics of segmentation methods.

## Abstract

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.