Exploring Diverse Approaches to Image Classification: A Comprehensive Review

EasyChair Preprint no. 11067

Abstract

Image classification is a study which is a part of computer vision and is defined as categorization of images into various classes or categories using the software. Out of so many image classification techniques available, it becomes difficult to choose a desired image classification technique to achieve our desired purpose. To understand when we should apply which image classification technique, it is important to understand how the individual image classification techniques work. The purpose of this research paper is to discuss some of the image classification techniques. There are many image classification techniques available but this research paper covers two image classification techniques each from the two main categories of image classification which are supervised image classification and unsupervised image classification. Here, we theoretically understand what are the techniques, how the techniques are implemented and the advantages and disadvantages of the techniques. Finally, we conclude this research paper by stating that a single image classification technique should not be considered when classifying images; rather, a mixture of various image classification techniques should be used to get lucid and vivid results.

Keyphrases: computer vision, Image Classification Technique, Lucid, vivid

@Booklet{EasyChair:11067,
  author = {Rishov Saha},
  title = {Exploring Diverse Approaches to Image Classification: A Comprehensive Review},
  howpublished = {EasyChair Preprint no. 11067},

  year = {EasyChair, 2023}}