Image Classification: An Artistic Science

Can you imagine a technology that allows computers to classify digital images by “seeing” them? The computing technique, image classification, helps make such a task possible.

In this article, we’ll deep dive to know about image classification.

Object recognition technologies function on the back of techniques like Image classification which help in categorizing objects from a pile of CCTV video footage.

What is Image Classification?

Image classification is the task of classifying images (pixels or vector groups) by assigning labels and categorizing them based on some rules. Remote sensing images rely heavily on this technology.

Other areas of application include agriculture, surveillance, disaster control, geographic mapping, and object detection.

Why is it important?

We are living in an era where data is gold.

With recent technological developments like Artificial Intelligence and the Internet of Things, a lot of data is generated.

Hence, the need to organize and process that data is becoming more important. Since data nowadays mostly includes photos, videos, with text or graphics, we need to classify it.

With a lot of data, comes a lot of responsibility.

Most of the data we get from cameras and sensors, is unstructured.

Therefore, to analyze an image we require complex Machine Learning algorithms. On the contrary, image classification is a technology that uses AI-based deep learning models that go beyond human capabilities to analyze digital images with the highest accuracy. Also, it is the most crucial part in some specific tasks like face recognition, license plate recognition, OCR, etc.

As visual information is sensitive, recent advancements are noticing a trend of shifting machine learning tasks from cloud to edge. Edge AI has made it possible to process image data and do ML computing tasks on edge computers connected directly to cameras rather than on the cloud.

Image classification is also a crucial process of image data analysis in Computer Vision models and hence paves the way to image recognition.

Moreover, real-world scenarios require better privacy, robustness and accuracy which is not possible in the cloud.

Photo by NASA on Unsplash

Image Classification as a basis of Computer Vision

Computer Vision enables computers to replicate high-level visual capabilities and 'see' the world the way we humans see it. But for that to happen, we need to solve a set of problems first.

Such problems or steps in computer vision may include image classification, image recognition, image segmentation or object detection.

Image classification is the most fundamental and crucial step in computer vision and also a foundation in solving other problems like one-shot learning.

Technologies like object detection are based on image classification, for instance, to detect a person it first needs to be classified as a human to proceed further. Hence, this technology has its application in a wide range of use cases like medical imaging, object identification in satellite images, traffic control systems or even brake light detection.

Supervised vs Unsupervised Image Classification?

There are two types of methods in classifying image data namely, supervised and unsupervised image classification. Supervised classification uses pre-fed training data in the form of previously classified reference samples to train the classifier in further classifying new and unknown image data.

However, unsupervised image classification is fully automated that discovers hidden patterns or data groups to analyze unlabeled datasets that too without the need for human intervention or any training data.

The most important methods in unsupervised image classification are pattern recognition and image clustering with K-means and ISODATA being the most popular algorithms.

In supervised image classification, samples of training data within the image are allocated a pre-defined category of, let’s say, roads, humans, buildings, or vegetation.        

How does this work?

The computer analyses an image in image classification by analyzing the pixels. Let’s get to know how it does it step by step:

  1. At first, the computer analyses the image statistically by considering it as an array of matrices of pixels with matrices depending on image resolution. Algorithms carry out analysis that depend on statistical data acquired above which results in the grouping of pixels in predefined categories or “classes”.
  2. Then, algorithms perform the next step of characteristic extraction that differentiate images according to their most prominent features. This aids the final classifier in forming the idea of which class the image may be considered into. The categorization of images is one of the most important steps, which forms the basis for other steps to follow.
  3. In Supervised classification, the result hugely relies on the pre-fed dataset. The more well-defined classification and good quality images, the more are the chances of accurate image classification.
Facial Detection Technology

Image Classification Using Machine Learning

Machine learning is the fastest-growing field as of, in the current scenario.

Image classification also makes use of this advanced technology to improve its accuracy. A popular machine learning technique called deep learning leverages hidden knowledge in the provided dataset using algorithms in the case of supervised classification.

The development of deep learning methods has revolutionized technologies like face recognition, image recognition, and real-time object detection. Advanced AI software and robust GPU’s have made it possible to achieve above human-level performance with image classification algorithms.

Why does deep learning win over traditional image classification methods?

Deep learning enables computers to learn on their own, the accuracy of a traditional image classification method is heavily reliant on the segmentation process and feature extraction process discussed above. However, deep learning is capable of learning the features of an image on its own without any human supervision. But it demands advanced infrastructure facilities and a large amount of labelled data or annotated images.

Many companies like Tooliqa are developing futuristic technologies involving Deep Learning and Computer Vision to innovate security systems.

Tooliqa is working towards merging 3D and Deep Learning technologies to revolutionize the interior design industry, where one of the algorithms are being designed to predict and recommend designs based on existing and customer-preferred designs.

Conclusion

Applications of Deep learning methods are growing every day and image classification is no exception.

Recent advances in machine learning models use algorithms to accurately figure out the features of an image, that too without human supervision.

Image classification has applications in self-driving cars and medical imaging, and we are not far away from a future where we could rely on machine’s safety.

Read also: The Future of Image Recognition | Insights - Tooliqa

Tooliqa specializes in AI, Computer Vision and Deep Technology to help businesses simplify and automate their processes with our strong team of experts across various domains.

Want to know more on how AI can result in business process improvement? Let our experts guide you.

Reach out to us at business@tooli.qa.

FAQs

Quick queries for this insight

How does image classification work?

Image classification is a process of assigning a label to an image. This label can be anything from the object in the image to the mood or emotion it conveys. To do this, algorithms are used to extract features from the image and compare them to a database of known images. The algorithm then makes a prediction based on the similarity of the features.

What is the difference between supervised and unsupervised image classification?

Both supervised and unsupervised image classification are types of picture data categorization techniques. The classifier is trained to categorise further new and unclassified picture data using supervised classification, which uses pre-fed training material in the form of previously classified reference samples. Unsupervised image classification, on the other hand, is completely automated and analyses unlabeled datasets without the need for any training data or human intervention. It does this by finding hidden patterns or data groups.

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