How Does Image Classification Work?

Have you ever wondered how computers deliver accurate image search results? Or how do mobile phones automatically tag your friends once you click their face in a group photo? These are only a few of the numerous things machines can do by integrating Artificial Intelligence (AI).

With computer vision and image recognition, AI or machine learning-enabled computers can process images by detecting and classifying them. Training these machines requires big data to use a high-quality image annotation tool to train the algorithms.

But how does image classification work in machine learning (ML)? You’ll find out the answer in this article. 

What is image classification? 

Machines can’t process images in the same manner as humans do. They rely on vectors or pixels to analyze one. However, with the help of AI or ML training models, they can be trained to identify and classify an image.

Electronically processing an image involves a machine receiving an input—in this case, a photo—and extracting its features to predict which pre-determined categories or class they belong to. Algorithms in annotation tools are used to create these labels and classifications, making the “training” algorithm and annotation tool the most crucial elements of image classification, alongside the volumes of data used to feed it. 

Image classification is used in various applications. In the auto industry, computer vision and image classification are utilized in self-driving cars, where the system automatically detects other vehicles, traffic lights, pedestrians, and road objects.

Steps involved in image classification

The image has to be pre-processed before the machine can identify and classify the objects within the image. Generally, these are the steps involved in ML-trained image classification:    

1. Preparing an image for processing

Machine learning models significantly depend on the data they’re being fed with. This means that poor input can lead to your project producing dismal results. That said, it’s necessary to enhance your photos and eliminate distortions. You can resize, rotate, make a grayscale of the image, and so many other things.  

For a machine, an image is composed not only of pixels but also of several layers, with each one helping the computer detect specific features. For instance, facial recognition analysis may involve a computer detecting lines on the first layer and putting the layers together until they can form the eyes, mouth, and full facial features.   

2. Detecting objects of interest

Object detection is also done to prepare the images before being forwarded to an ML-backed classifier. Several methods can be used depending on the number of objects within the image.

Image localization is often used when there’s a single object of interest in the frame. Computers process an image by looking at the pixels and their numerical values that have corresponding colors and hues. Once the object of interest has been identified, it’s enclosed in a bounding box to indicate the essential part of the photo and the pixel values that define the object. Conversely, object detection is used in cases involving several objects of interest.         

3. Model training and object classification

After the image data has been pre-processed and tagged, it’s forwarded to the ML algorithm for “training.” The model processes the image in this stage by analyzing the datasets to enable model training. The machine learns to detect different or similar patterns and identify unifying features in a specific class.        

Multiple image classification algorithms are being used, including:

• K-Nearest Neighbors (KNN)
• Multi-Layer Perceptrons (Neural Nets)
• Support Vector Machines
• Convolutional Neural Networks (CNN)

There’s no one-size-fits-all solution when it comes to these algorithms. But CNN seems one of the best options as it is patterned after the neurons in the human brain. 

Training can either be done through supervised or unsupervised learning.

• Supervised learning - This involves the developer labeling and creating the classes or categories and validating whether the generated outcome is valid. Vector-based algorithms and CNN-backed machines can be used to detect and identify the objects in the image.
• Unsupervised learning - This method has less human intervention, and a machine can start doing its job after being fed with pre-tagged datasets. The computer searches for the pre-set categories, pixel patterns, and other inputs in analyzing the object. This approach can work through CNN layers and compares a reference image from its database against the image input.

And while unsupervised learning may sound like an attractive option to some developers, humans can’t control the training or supervise the new classes that the computer might generate, possibly leading to inaccuracies and deficiencies. 

4. Validating the algorithm

After gathering a high-quality dataset and choosing the best model for the classification task, it’s time to train and validate the output provided by the model.

Concluding thoughts

Model training is meant to allow the machines to become better as they’re being fed with more data. It’s also supposed to learn from its past errors. These features make ML-backed image classification highly flexible and capable of evolving to human needs. However, AI developers must have high-quality datasets and a reliable annotation tool to generate the ideal outcomes.

References

1. Nelson, D. 02 August 2021. How Does Image Classification Work?. www.uniteai.com.   https://www.unite.ai/how-does-image-classification-work/. Accessed 14 July 2022
2. Jain, T. Undated. Basics of Image Classification Techniques in Machine Learning. https://iq.opengenus.org/. https://iq.opengenus.org/basics-of-machine-learning-image-classification-techniques/. Accessed 14 July 2022
3. Bonner, A. 02 February 2019. The Complete Beginner’s Guide to Deep Learning: Convolutional Neural Networks and Image Classification. www.towardsdatascience.com. https://towardsdatascience.com/wtf-is-image-classification-8e78a8235acb. Accessed 14 July 2022