We live in a world of digital clutter where we take 7,200,000,000,000 new images every year.
Most of the image data coming from our cameras and sensors are unstructured.
When it comes to personal use, we do not need to understand what these images imply.
However, for real-world scenarios where images can present unrivaled insight into any situation, they are worth analyzing.
We rely on advanced processing techniques to analyze images efficiently for all such uses.
One common image processing technique we all have heard of is image classification.
But, there is more about image classification than you already know- that sure is a bit technical but essential to learn.
So, whether you are a beginner in computer vision or someone exploring this field to start with a project, we suggest you read this article explaining the image classification technique.
Explaining Image Classification as a Computer Vision Technique
In computer vision, image classification is the task of categorizing pixels by assigning labels to them. This technique helps machines predict the class of an unknown data instance (an image) based on what is known (sample images).
So, the known data is the legion of images fed to models while training them.
Based on that fed data and AI-based deep learning models, image classification analyzes images for complex tasks that surpass our accuracy level.
The tasks include object detection in satellite images, machine vision, traffic control systems, medical imaging, brake light detection, and more.
Image classification applications use classification methods that are mainly two in number to classify image data.
2 Classification Methods
1. Unsupervised Classification
An unsupervised classification technique is easy to segment an image because you do not need training models.
Instead of using training data sets, this technique leverages machine learning algorithms to group pixels into clusters and classify each pixel. It helps uncover hidden patterns or data groups without our intervention.
Unsupervised classification happens in two basic steps:
- Creating clusters
- Assigning labels
Using classification software, we first create clusters. Two common clustering algorithms you can use are:
Once you choose a clustering algorithm, you can decide how many clustering groups you want to generate. For example, you can pick 18, 20, or 40. With fewer clusters, you will have more similar pixels in a group. But, more clusters will increase variation in groups.
Either way, they are unclassified clusters. So, the next step is to assign labels to classes manually.
2. Supervised Classification
In supervised classification, you need to provide datasets as input to the software. The datasets are pixels that are representative of particular classes. You select these pixels in the image as representative samples of the classes you want to identify.
The software takes samples as references and applies them to the entire image to classify other pixels.
Supervised classification follows three basic steps to give you the results:
- Selection of training areas
- Generating signature file
The process starts with creating training samples for supervised classification. For example, you can mark an area in an image. Then, continue adding representative areas of the image as sample datasets. Keep creating samples until you have a representative area for each class.
In turn, the software will generate a signature file with all training sample information stored in it.
Finally, you can use the signature field to run a classification. Here, you will need to select a classification algorithm from the following:
- Maximum likelihood
- Support vector machine (SVM)
- Principal components
- Iso cluster
In most cases, a support vector machine (SVM) is one of the best classification algorithms to pick. However, each option comes with its own advantages, which we leave to you to explore yourself.
From these classification processes, the final output comes in the form of classified maps.
In computer vision tasks, image classification and other techniques such as instance segmentation, object detection, and image processing play a significant role. They give machines the ability to understand what an image represents.
Since our devices generate vast amounts of unstructured data, which is of immense value and needs to be analyzed, we need these classification techniques to run analysis efficiently.
Though brief, the discussion above explaining image classification and the two methods used for classifying images is enough for you to get acquainted with the basics of analyzing images.
With these fundamentals in mind, you can explore the methods further to create and train your own models and begin addressing any computer vision-based machine learning problems.