Exploring Diverse Approaches to Image Classification: a Comprehensive Review

EasyChair Preprint no. 11384

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:11384,
  author = {Savita Sangam and Rishov Saha},
  title = {Exploring Diverse Approaches to Image Classification: a Comprehensive Review},
  howpublished = {EasyChair Preprint no. 11384},

  year = {EasyChair, 2023}}