Human beings have the innate ability to distinguish and precisely identify objects, people, animals, and places from photographs. However, computers don’t come with the capability to classify images. Yet, they can be trained to interpret visual information using computer vision applications and image recognition technology.
As an offshoot of AI and Computer Vision, image recognition combines deep learning techniques to power many real-world use cases. To perceive the world accurately, AI depends on computer vision.
Without the help of image recognition technology, a computer vision model cannot detect, identify and perform image classification. Therefore, an AI-based image recognition software should be capable of decoding images and be able to do predictive analysis. To this end, AI models are trained on massive datasets to bring about accurate predictions.
According to Fortune Business Insights, the market size of global image recognition technology was valued at $23.8 billion in 2019. This figure is expected to skyrocket to $86.3 billion by 2027, growing at a 17.6% CAGR during the said period.
What is Image Recognition?
Image recognition uses technology and techniques to help computers identify, label, and classify elements of interest in an image.
While human beings process images and classify the objects inside images quite easily, the same is impossible for a machine unless it has been specifically trained to do so. The result of image recognition is to accurately identify and classify detected objects into various predetermined categories with the help of deep learning technology.
How does AI Image Recognition work?
How do human beings interpret visual information?
Our natural neural networks help us recognize, classify and interpret images based on our past experiences, learned knowledge, and intuition. Much in the same way, an artificial neural network helps machines identify and classify images. But they need first to be trained to recognize objects in an image.
For the object detection technique to work, the model must first be trained on various image datasets using deep learning methods.
Unlike ML, where the input data is analyzed using algorithms, deep learning uses a layered neural network. There are three types of layers involved – input, hidden, and output. The information input is received by the input layer, processed by the hidden layer, and results generated by the output layer.
As the layers are interconnected, each layer depends on the results of the previous layer. Therefore, a huge dataset is essential to train a neural network so that the deep learning system leans to imitate the human reasoning process and continues to learn.
[Also Read: The Complete Guide to Image Annotation]
How is AI Trained to Recognize the Image?
A computer sees and processes an image very differently from humans. An image, for a computer, is just a bunch of pixels – either as a vector image or raster. In raster images, each pixel is arranged in a grid form, while in a vector image, they are arranged as polygons of different colors.
During data organization, each image is categorized, and physical features are extracted. Finally, the geometric encoding is transformed into labels that describe the images. This stage – gathering, organizing, labeling, and annotating images – is critical for the performance of the computer vision models.
Once the deep learning datasets are developed accurately, image recognition algorithms work to draw patterns from the images.
Facial Recognition:
The AI is trained to recognize faces by mapping a person’s facial features and comparing them with images in the deep learning database to strike a match.
Object Identification:
The image recognition technology helps you spot objects of interest in a selected portion of an image. Visual search works first by identifying objects in an image and comparing them with images on the web.
Text Detection:
The image recognition system also helps detect text from images and convert it into a machine-readable format using optical character recognition.
The Importance of Expert Image Annotation in AI Development
Tagging and labeling data is a time-intensive process that demands significant human effort. This labeled data is crucial, as it forms the foundation of your machine learning algorithm’s ability to understand and replicate human visual perception. While some AI image recognition models can operate without labeled data using unsupervised machine learning, they often come with substantial limitations. To build an image recognition algorithm that delivers accurate and nuanced predictions, it’s essential to collaborate with experts in image annotation.
In AI, data annotation involves carefully labeling a dataset—often containing thousands of images—by assigning meaningful tags or categorizing each image into a specific class. Most organizations developing software and machine learning models lack the resources and time to manage this meticulous task internally. Outsourcing this work is a smart, cost-effective strategy, enabling businesses to complete the job efficiently without the burden of training and maintaining an in-house labeling team.
The Process of Image Recognition System
The following three steps form the background on which image recognition works.
Process 1: Training Datasets
The entire image recognition system starts with the training data composed of pictures, images, videos, etc. Then, the neural networks need the training data to draw patterns and create perceptions.
Process 2: Neural Network Training
Once the dataset is developed, they are input into the neural network algorithm. It acts as a premise for developing the image recognition tool. Using an image recognition algorithm makes it possible for neural networks to recognize classes of images.
Process 3: Testing
An image recognition model is as good as its testing. Therefore, it is important to test the model’s performance using images not present in the training dataset. It is always prudent to use about 80% of the dataset on model training and the rest, 20%, on model testing. The model’s performance is measured based on accuracy, predictability, and usability.
Top Uses Cases of AI Image Recognition
Artificial intelligence image recognition technology is increasingly used in various industries, and this trend is predicted to continue for the foreseeable future. Some of the industries using image recognition remarkably well are:
Security Industry:
The security industries use image recognition technology extensively to detect and identify faces. Smart security systems use face recognition systems to allow or deny entry to people.
Moreover, smartphones have a standard facial recognition tool that helps unlock phones or applications. The concept of the face identification, recognition, and verification by finding a match with the database is one aspect of facial recognition.
Automotive Industry:
Image recognition helps self-driving and autonomous cars perform at their best. With the help of rear-facing cameras, sensors, and LiDAR, images generated are compared with the dataset using the image recognition software. It helps accurately detect other vehicles, traffic lights, lanes, pedestrians, and more.
Retail Industry:
The retail industry is venturing into the image recognition sphere as it is only recently trying this new technology. However, with the help of image recognition tools, it is helping customers virtually try on products before purchasing them.
Healthcare Industry:
The healthcare industry is perhaps the largest benefiter of image recognition technology. This technology is helping healthcare professionals accurately detect tumors, lesions, strokes, and lumps in patients. It is also helping visually impaired people gain more access to information and entertainment by extracting online data using text-based processes.
To train a computer to perceive, decipher and recognize visual information just like humans is not an easy task. You need tons of labeled and classified data to develop an AI image recognition model.
The model you develop is only as good as the training data you feed it. Feed quality, accurate and well-labeled data, and you get yourself a high-performing AI model. Reach out to Shaip to get your hands on a customized and quality dataset for all project needs. When quality is the only parameter, Sharp’s team of experts is all you need.