The evolution of AI and image recognition

There's a lot of excitement when it comes to developments in AI and image recognition technology. The ability of machines to interpret, analyze, and assign meaning to images is a key area of interest and innovation.

Companies across industries are rapidly adopting image recognition technologies for a wide variety of purposes. A huge part of this progress has become possible due to the ever-increasing number of digital photos and videos uploaded online by people all over the world. It helps make visual data processing and analysis capabilities faster, more accurate, and more efficient.

Healthcare, marketing, transportation, and e-commerce are just a few of the many applications of today's applications of this technology. Emerging technologies like augmented reality, virtual reality, and computer vision applications are all based on AI image recognition. It's even been prominently featured in Hollywood blockbusters — from the 1980's classic Robocop to Blade Runner.

If you're looking to learn more about artificial intelligence and image recognition, you've come to the right place. In this post, we explore using AI in more detail based on insightful image recognition examples to address the following questions: What is image recognition in AI? And how does it work?

To find out where we're going, it's important to understand where we've been — and how this technology has developed into what it is today, along with its potential future uses. As we dive into key terms, current uses, and future applications, we also take a closer look at the evolution of this rapidly growing technology to date.

Key terms of the image recognition technology

First, let's start off by defining some key terms so that we can better understand how they're related to one another and how they contribute to the development of AI as a whole.

Image recognition involves the creation of a neural network that processes the individual pixels of an image. In other words, it's a type of AI programming that can "understand" the content of an image by analyzing and interpreting pixel patterns. Researchers feed these networks with as many pre-labeled images as possible to "teach" them how to recognize similar images.

Image recognition is a subcategory of computer vision, which is an all-encompassing descriptor for the process of training computers to "see" like humans and take action. Even without realizing it, we frequently engage in mundane interactions with computer vision technologies like facial recognition. Image processing is a sweeping term for using machine learning algorithms to analyze digital images.

Any AI system that processes visual information generally relies on computer vision — and those systems that can identify certain objects or categorize images based on their content are performing AI image recognition. This is critical for machines that need to recognize and categorize different objects around them accurately and efficiently. For example, driverless cars that use computer vision to identify pedestrians, traffic signs, and other vehicles in the vicinity.

A related term, pattern recognition, is a broader concept compared to computer vision which focuses on image recognition. However, image recognition can be described as a common application of pattern recognition where a computer vision system is trained to recognize patterns in images, and then identify images that contain those patterns. Valuable use cases include identifying faces in photos, recognizing and classifying objects, finding landmarks, and detecting body poses or keypoints.

Data inputs for pattern recognition can be words or texts, images, or audio files. As a compilation of loosely related areas and techniques, pattern recognition analyzes incoming data and tries to identify patterns. It's an absolute must for intelligent systems such as CAD systems in medicine. Other techniques include speech recognition, text classification, and automatic recognition of images of human faces or handwriting.

Image recognition is sometimes confused with image detection, which involves taking an image as input and finding various objects within it — think face detection, where algorithms aim to find facial expressions and patterns in images. While image detection aims to distinguish one object from another to identify how many separate entities there are within an image, image recognition focuses on identifying the objects of interest within an image and recognizing which category or class they belong to.

A brief history of computer vision

The first steps toward what would later become image recognition were taken in the late 1950s. However, computer vision as an academic discipline really took off in the 1960s at universities that were pioneering the development of AI. Early researchers recognized the potential of AI to change the world. With the goal of imitating human brain and eye sight, computer vision was brought to life through a summer project in 1966 when researchers attached a camera to a computer and had it "describe what it saw" — kicking off a new and exciting stage of development.

What made computer vision a cutting-edge prospect at the time was the goal of extracting 3D structures from images to achieve a complete understanding of the scene. Studies from the 1970s formed the basis of many of the computer vision algorithms we use today, such as extracting edges, labeling lines, representing objects as interconnections of smaller structures, and so on. Later studies evolved to incorporate more intense mathematical and quantitative analyses — driving progress and innovation forward. These included scale-space, contour models, detecting shape based on shading, texture, focus, and more.

The 1990s ushered in a new stage of growth including projective 3D reconstructions that led to greater awareness of camera calibration, which in turn, led to new methods for reconstructing scenes from multiple images. Variations of graph cuts were used to solve image segmentation and more. A major transition came about with the increased interaction between computer graphics and computer vision, including image-based rendering, image morphing, panoramic image stitching, and light-field rendering. No doubt you've heard of some of these terms already.

Present day examples of research and innovation include the advancement of deep learning techniques that have propelled computer vision to a new level — increasing the accuracy of algorithms on data sets for image classification, image segmentation, optical flow, and more.

AI image recognition of today and future applications

Image recognition today is carried out in a variety of ways, but most methods involve the use of supervised learning, neural networks, and deep learning algorithms. Convolutional neural networks help ML-based systems improve their ability to identify an image's subject.

One of the most promising areas of research and development is on new and emerging technologies that have the potential to revolutionize many industries and improve the quality of life for people everywhere — from healthcare, including more precise diagnoses of diseases, to finance, through fraud detection based on image analyses of banknotes.

Below are several examples of future applications of this technology:

• Refining augmented reality — Primarily in software and game development for more realistic experiences. The gaming industry has been making significant strides in this area, but image recognition software development is not just limited to this one industry. It also serves as the foundation for applications in advertising, which use augmented reality.

• Enhancing medical imagery — Images make up the primary source of data for the healthcare industry. Smart picture recognition systems will be able to train these medical photos while improving diagnoses and early detection practices.

• Empowering educators — Image recognition algorithms make it possible for students with learning disabilities to record their knowledge. For example, text-to-speech and vision-based programs.

• Advancing self-driving cars — An exciting development in this area is that researchers are close to creating AI that would enable cars to see in the dark thanks to the image recognition algorithm.

• Teaching machines to see — Teaching machines to recognize visuals, analyze them, and make decisions based on visual input has tremendous potential for production around the world, as seen in industrial and manufacturing processes already.

• Improving facial recognition — Facial recognition technology is frequently used for biometric identification, where a person's identity is verified by scanning their facial features. The advancement of image recognition techniques is bringing about new possibilities for facial recognition use across industries with improved accuracy and novel applications.

Of course, apart from these, there are many other advances and future applications for AI. The possibilities are truly limitless.

AI image recognition technology

As mentioned, AI-based technologies have grown in significance across industries such as healthcare, retail, security, agriculture, and more.

Below we explore some common applications of these technologies:

Facial recognition and analysis

This includes facial identification, recognition, and verification using cameras or webcams. With the help of AI algorithms, the latest software has made countless applications possible including face detection, alignment, and pose estimation, as well as gender recognition, smile detection, and age estimation using deep convolutional neural networks.

Medical image analysis

Visual recognition technology helps computers to understand visual data that is routinely acquired throughout the course of a patient's treatment; for example, detecting a bone fracture.

Animal monitoring

In agriculture and farming, AI image recognition algorithms are used to observe animals and other livestock for diseases, anomalies, as well as for compliance with animal welfare standards, industrial automation, and more.

Pattern and object detection

AI photo and video recognition technologies can be used to identify objects, people, patterns, logos, places, colors, and shapes. And the image recognition aspect of these technologies can be customized across software. For example, if a model is programed to detect people in a video frame, it can then be applied to people counting as used in retail.

Image-based plant identification

Used widely in research, nature management, and sustainability efforts, image recognition systems can also help identify plant species, monitor for diseases, and track growth cycles. Likewise, such systems can map crop quality.

Food recognition

As seen in computer-aided dietary assessments, image recognition works to improve the accuracy of dietary intake measurements by analyzing food images taken on digital devices and shared online.

Image search

Also referred to as visual search, image search uses visual features learned from a deep neural network to develop organized and scalable ways for retrieving images with the broader aim of carrying out content-based image retrieval.

Production-line quality assurance

Applied primarily in the production and manufacturing sector for testing and inspections, an image recognition system can also be used for quality assurance by helping to detect product defects or flaws.

Automobile manufacturing

Think autonomous vehicles. Image recognition plays a significant role in how successfully self-driving cars can navigate their environment without a person sitting behind the wheel. Perfecting this technology would be a breakthrough in the way we drive.

Security and surveillance

The technology can be used to train a computer to identify people or objects based on their appearance, while giving security personnel a break from having to monitor multiple displays at once.

Automation of administrative processes

Paying bills, scheduling appointments, collecting data and any other type of repetitive or monotonous task has the potential to be automated with the help of several AI methods including image recognition systems.

Asset management and project monitoring

In energy, construction, rail, or shipping, for example. Defects such as rust, missing bolts and nuts, damage or objects that do not belong where they are can be identified with the help of object detection and object recognition.

These are just some of the many applications, but there are countless other ways in which this cutting-edge technology can be put to good use.

Which image recognition model to choose?

Pretrained image recognition models that are based on Convolutional Neural Networks (CNN) are at the center of AI image recognition technology. Another key element of image recognition is having the right training data, which must be collected, annotated, and fed into these models to retrain and fine-tune them for specific downstream applications. Accuracy is the main benchmark for evaluating image recognition tools. Factors like speed and adaptability are usually considered at a later point.

Common CNN-based pretrained models for image recognition work include:

• Faster R-CNN (Region-based CNN) — A two-stage pretrained model that uses a CNN to produce candidate object regions, which are then passed through a separate CNN to classify images and refine bounding boxes. Accuracy is the key benefit, but it can take a long time to retrain.

• You Only Look Once (YOLO) — A one-stage model that uses a CNN to predict class labels and bounding boxes of objects in an image. The main advantage is fast inference time (or quick delivery) and low memory usage. On the downside, it's less accurate compared to Faster R-CNN.

• Single Shot MultiBox Detector (SSD) — A one-stage model that uses a single CNN to predict bounding boxes and class labels of objects in an image. It has a good balance between accuracy and performance speed.

In terms of model evaluation, deployment, and monitoring, human annotators play a key role in gaging the performance of AI-assisted image recognition solutions when faced with new, previously unseen data. That's where Toloka's crowd contributors come into play.

Data markup and image annotation on Toloka

When it comes to training data labeling for AI-assisted image recognition applications, crowdsourcing helps to distribute image annotation tasks among hundreds or thousands of annotators — quickly, efficiently and at a low cost.

Toloka's crowd contributors can complete the following image labeling tasks:

Image segmentation

This involves object recognition and drawing pixel-wise boundaries for each object or group of objects.

Bounding box

Identifying objects in images that match certain classes and using bounding boxes to mark the location.

Polygon

Identifying objects in images that match certain classes and drawing pixel-perfect polygons around the exact shape.

Keypoint

Labeling feature details in human faces to identify facial landmarks, expressions, or emotions.

Image classification

Image classification is done by matching visual content with one or more predefined categories.

Image transcription

Transcribing text in PDF files and using labeled data to train text recognition algorithms or validate and fine-tune the output of OCR models.

Side-by-side comparison

Using side-by-side image comparisons to verify or clean up data, looking at two images and picking the one that's better.

Image and video collection

Collecting datasets of videos or images related to a common theme, or with a specific time of lighting or environment.

Visit our blog to learn more about the benefits of crowdsourcing and to discover what other types of data labeling tasks Tolokers are involved in when it comes to the wider machine learning pipeline.

Key takeaways

As a recap, image recognition essentially means identifying objects within an image and categorizing the image correspondingly. Image, photo, and picture recognition are all basically the same thing. In this article, we've defined image recognition as an application of AI and how it relates to computer vision, while covering everything from the origins of this technology to future scenarios and opportunities.

Obviously, image recognition seems like a simple task to us humans. If you look at an object or scene in an image, you can automatically make distinctions between subjects and identify what you see. For a machine, however, this is highly complex, which makes AI image recognition a long-standing research topic in the field of computer vision.

As different methods to simulate human vision have evolved over time, the main idea behind image recognition has stayed the same: the ability of machines to classify objects into different categories — and determine the category to which an image belongs.

Image recognition combined with deep learning is a key application of today's AI vision and is used to power a wide range of real-world use cases. Recent advances have led to great results across computer vision and image recognition tasks. And no doubt that progress will continue for years to come.