Deep Learning Applications in Image Recognition
Humans are visual creatures and it shows in much of the technology we create. From UX design to photography, our reliance on sight can’t be overstated which is why Deep Learning is becoming increasingly popular for visual applications.
Using Artificial Intelligence (AI) and convolution, deep learning algorithms are seemingly everywhere in our modern technology. Automated systems like self-driving cars use computer vision to navigate the road while simple camera filters scan facial features for our favorite selfies posted on social media. So how is Deep Learning being applied to image recognition?
Historical Context
Before AI, image recognition was based on rudimentary methods that involved template matching. This strategy involved matching specific images to a broader template. Onlookers would then scan for inconsistencies in size, orientation, or lighting and note them down. While straightforward, the method was highly inefficient because of the intense scrutiny that observers had to provide, often missing more subtle differences.
Deep Learning, and more specifically convolutional neural networks, were able to solve this dilemma with automation that analyzed the details of every pixel in an image. This paved the way for greater feature learning which automated the extraction and comparison of certain edges and colors in an image.
What is Deep Learning?
Deep learning is a subfield of AI that places layers of nodes between the input and output layers of a machine learning algorithm. This gives AI models more processing capabilities, allowing them to make better judgements and decision-making based on larger data sets collected from online activity.
The layers of nodes between the input and output are known as neural networks because of the web of processing activity that they create, mimicking the human brain. These advanced algorithms also can feed data backwards, in a process known as backpropagation, which helps reinforce self-learning evaluation.
Key Deep Learning Architectures in Image Recognition
Since the development of deep learning, there have been many exciting innovations in image recognition technology using specific algorithms. Examples include:
Convolutional Neural Networks: CCNs analyze grid-like data to process data. These grids are perfect for image recognition because they utilize the same dimensional space as 2D and 3D imagery.
Deep Belief Networks: DBNs are some of the earliest forms of deep learning, using multiple layers of latent variables to execute generative tasks and feature reduction, making them highly versatile.
Autoencoders: Autoencoders create a bottleneck feature by compacting input data, feeding it through a neural network, and then decompressing it afterwards. This allows algorithms to be featured on a large scale with multiple compact inputs.
Generative Adversarial Networks: GANs represent a dual model that involves a generator and a discriminator. The generator creates an image and the discriminator determines whether it is real or not. Both models evaluate and improve themselves to outperform the other.
Major Applications of Deep Learning in Image Recognition
The application of deep learning has transformed entire industries because of its vast amount of use cases. Example include:
Face Recognition: One of the most common forms of deep learning, facial recognition can be seen everywhere from social media posts and tags to advanced security surveillance cameras used by police and military.
Medical Imaging: X-rays and MRIs have taken huge leaps forward with deep learning, helping doctors spot medical conditions before they become serious health threats.
Autonomous Vehicles: Self-driving cars exhibit some of the most sci-fi-like qualities, but have quickly become a reality with convolution and computer vision, giving cars the ability to navigate roads safely.
Augmented Reality: Digital overlays are becoming increasingly popular in games like Pokemon Go which superposes digital worlds over local streets.
Retail & Fashion: Automated tagging is making online shopping easier by helping users purchase clothing from their favorite social media posts and trying them on virtually to see if they want the outfits for themselves.
Advantages of Using Deep Learning for Image Recognition
Deep learning presents many clear benefits to image recognition that would be impossible to do manually otherwise. This is because computers have the power to process specific details using exact measurements. Not only can deep learning models analyze images pixel by pixel, but they can also process each layer, understanding the intricacy of every detail including shading and lighting.
AI algorithms also have the ability to scale to a degree that is impossible for humans. Online activity generates massive amounts of big data daily and would take humans an incredible amount of time to process on their own. By using deep learning, these algorithms can analyze the countless amounts of images and videos uploaded in real-time to platforms like YouTube and TikTok to output more accurate results.
Challenges and Limitations
Despite their vast benefits, there are still many obstacles that need to be solved to improve deep learning algorithms. One of the largest challenges is their intense reliance on data. Without large datasets, deep learning algorithms can’t function properly because they lack the foundation to generate accurate results.
This has become an issue lately because data collection methods have become a hotly debated topic which has led to websites being required to provide an opt-out option for users. To some, the tokenization and sale of private/personal data and traffic activity is an ethical breach, stunting the ability for data to be used for AI training.
Future of Deep Learning in Image Recognition
There are many exciting possibilities for the future of deep learning and image recognition. Improvements in unsupervised learning and neural architecture search mean that algorithms can improve their performance exponentially with new frameworks and designs specifically tailored to image recognition.
Exciting new applications also exist such as remote sensing which can be installed into robotics to help monitor and track natural environments to help combat climate change. Improved editing software can also help spur content creation, helping creators develop more engaging series on social media platforms like YouTube.
