The Evolving Uses of Image Recognition
Machine learning is one of the biggest buzzwords of this decade, heralding a new revolution in artificial intelligence and intelligent computing. One of the biggest beneficiaries of the recent development in machine learning, specifically deep learning, has been image recognition. For years, image recognition took an algorithmic approach to decipher objects in images. With deep learning, learning algorithms such as neural networks are being trained to recognize images through millions of examples to bring image recognition into a new era.
Most people are surprised to learn that neural networks are an old concept dating back to the 60s, although their usability was severely limited by the limited processing power of CPUs of that era. Their recent dominance has been mainly due to the use of GPUs in drastically cutting down the time required to train neural networks. This means today’s neural networks can have significantly more capacity and therefore learning capability, while also being trained in a fraction of the time thanks to parallelization through GPUs. This is especially relevant for convolutional neural networks that have had a profound impact on computer vision and image recognition in particular.
Over the last few years, a lot of this development in image recognition (courtesy of deep learning techniques) has seeped into our lives. The impact can be seen in a range of scenarios, from current consumer devices such as smartphones to more traditionally creative ventures such as marketing and advertising. Here are some contemporary and emerging uses of image recognition in our modern world:
Truly Smart Phones
Today’s smartphones promote a range of AI and image recognition functionality to make them simpler to use and more useful to the consumer. Image recognition has been integrated with traditional camera apps to recognize the subject matter in the image automatically and adapt the camera settings to best suit the scenario.
The image recognition capabilities are also being integrated into the traditional photo organization apps. With better image recognition, these apps can organize photos by subject and even automatically add captions, tags and labels.
Better image processing and recognition capabilities backed by deep learning are also transforming the security industry. Recognizing people and their activities in real-time surveillance is more accurate than ever, helping to take on the security challenge in an increasingly volatile world. You can also see this technology trickle into consumer gadgets, being leveraged by companies like Apple and Samsung to provide better Face ID tech that not only recognizes your face but also takes into account more discreet features such as your iris pattern to provide an unparalleled level of security.
Organizing Visual Databases for Websites
Image data is the bread and butter for a lot of websites which rely on them to generate traffic. Typical examples of this include stock photography websites such as Shutterstock and Getty Images. Naturally, maintaining and labeling images in such huge databases is a logistical nightmare. With deep learning empowered image recognition methods, organizing such massive databases is easier than ever through AI generated tags and captions. This also brings a lot of value for customers as it is easier to navigate through these databases and related suggestions are more relevant.
Revolutionizing Digital Marketing
Brands and marketers are finally stepping into the AI game to facilitate customers in a smarter way. They are doing this in mainly two ways: mobile commerce and using image recognition to gauge customer engagement. Mobile commerce apps are allowing users to snap a photo of an object and find vendors selling it. This offers a lot of value to consumers where traditional search engines fail. E-commerce giant eBay has already integrated this into their search capabilities, allowing users to search through their listings with a photo of the object they wish to buy.
An important aspect of marketing is gauging customer’s responses to a marketing campaign or new product. Lots of users now rely on visual mediums such as photos and videos shared on social media to do this. Image recognition is playing a major role in helping marketers, allowing them to independently gauge a customer’s feedback by recognizing facial expressions.
Powering Self-Driving Cars
Self-driving tech has come a long way in recent years, evolving from simplistic cruise control to cars that can drive themselves in complex environments with no human assistance. The biggest driver for this evolution is improved image recognition that helps a car to observe road conditions and obstacles and navigate through them. Such software is an always evolving, learning system that grows more intelligent over use. In such safety-critical applications, GPUs are a necessity not only because of the sheer volume of image and video data generated by vehicles but also latency requirements that require training of deep learning system to be as quick as possible. Such tech is also seeping into more mainstream vehicles, with features such as pedestrian and sign detection fast becoming standard innovations thanks to better image recognition.
GPU Solutions — Turnkey Setups From Exxact
All in all, deep learning architectures have raised the accuracy and usability of image recognition systems to new heights. As these systems see more use and deployment in different scenarios, GPUs will play a critical role in helping lower training times and process more data. This makes it essential for companies developing and leveraging image recognition systems to have GPU compute clusters as a part of their arsenal to truly maximize the advantage that AI brings to their business operations.
The Exxact Deep Learning Development Box is one of our deep learning research signature offerings, powered by state-of-the-art NVIDIA GPU processing power.
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