Applications of CRF in segmentation, localization, object detection, sequence labeling, etc.

Author: Zen and the Art of Computer Programming

1 Introduction

In the fields of modern computer vision, image processing, etc., traditional methods based on artificial neural networks (ANN) have been unable to achieve good results, so many improved methods have emerged, such as deep learning methods (CNN, RNN), maximum entropy model ( ME), conditional random field (CRF), and other neural network structures, such as densely connected networks (DenseNet). These methods effectively improve the performance of the model by learning the data, obtaining the feature representation and performing prediction or classification. The conditional random field (Conditional Random Fields, CRF) is a probabilistic graphical model for modeling the relationship between variables, which can handle both state space and observation sequence. It exploits the regularization and local properties of graphical models to model observation sequences, forming a probabilistic model with global parameters.
This article will elaborate on the application of CRF in different fields such as image segmentation, positioning, object detection, and sequence labeling through the following aspects:

1. Segmentation and Semantic Segmentation
2. Localization and Object Detection
3. Sequence annotation
4. other applications

2. Explanation of basic concepts and terms

1. Concept

1.1 Random Field

A "random field" is a term in statistical physics used to describe a joint distribution of random variables, each influenced by some other random variable. In short, a function f