What is Image Recognition Technology?
We all are aware of facial recognition technology. The technology is used everywhere including offices, malls, etc. The high-resolution cameras are installed that use machine learning to detect certain faces. These faces are identified by matching the pixels and movements with the data available in the database.
But why only facial recognition when there can be image detection of everything? Let us take some example. You visit a party. You see a person with a really cool jacket. There is only a logo of that brand. What do you do if you do not know the logo but desperate to buy the same jacket? Well, asking the person directly is a bit odd and awkward.
Well, you can simply take out your smartphone and turn on the camera that supports image recognition. Point the camera towards the jacket and it tells you the brand name. It also shows you links to e-commerce sites where you can get the same jacket. Isn’t that amazing?
Let us take another example. You are in a garden and see a beautiful flower. And you wish to know more about that flower. You can increase your botany knowledge by pointing the camera towards the flower and get detailed information about the flower.
How does it Work?
Most of the latest technologies depend on machine learning and artificial intelligence. Image recognition uses Neural networks. Neural networks use layered algorithms contingent on the outcomes of the other surrounding algorithms. This process allows machines to simulate the logical reasoning used by humans. A system using convolutional neural networks (CNN) processes information related an image by labeling, predicting, and recognizing specific patterns.
Convolutional neural networks break down images into numbers. Convolution is nothing but a combination of two functions that produce a third function. CNN merges multiple sets of information and pools them together to build an accurate representation of an image.
On the basis of the pooling and all the data, computers can predict what the image is about. Computers take time to make accurate predictions. Just the way machine learning works. Computers practices making predictions from available datasets and information. It also uses experience in real-world situations.
Most widely used database for artificial intelligence is ImageNet. This database has 3.2 million images and allows technology experts to develop their own algorithmic models. It is predicted that the image recognition market will grow to 38.92 billion US dollars by the year 2021.
Here are some amazing benefits of image recognition technology:
More than 85 percent of brand logos posted on social media do not contain any kind of image tag or text mention. If marketers use tags for images, the Google crawlers can understand what is the image about. But with image recognition technology, this extra work can be skipped by marketers. All they have to do is add images and AI can help search engines understand what the image is about. Search engine crawlers can fetch and match datasets to identify images.
2. Mobile app development:
Every mobile app development agency can use image recognition technology to add value to the app. There are amazing mobile apps that make use of image recognition technology. Some of the apps are Lose It! Cam Find, Google Lens, Leafsnap, SnapFindShop, etc.
3. Education industry:
The education department can benefit a lot from image recognition. Students can learn about anything they wish using Google lens or similar apps. Theory and practical has different impacts on studies. It is believed that theory without practice is not much effective. Students can get information about any object or even about any subject in real time with image recognition.
4. Driverless cars:
How do you think cars understand traffic signals? How do you think cars understand parking blocks? Image recognition technology is used to help robot cars understand what the objects are.
The technology is used by IBM to detect diseases faster than ever before. The company is going to process massive quantities of medical images to diagnose diseases faster and with accuracy.
6. E-commerce industry:
SnapFindShop is the best example of e-commerce using image recognition. Since the information technology market is seeking great heights, businesses should understand the significance of voice search and image search. An e-commerce website has millions of products listed. It is highly important to add tags to those images and save the datasets in image recognition databases. Then only users can click photos to find similar products on e-commerce websites.
7. Gaming industry:
Image recognition can bring revolutionary changes in the gaming market. Game app developers are well aware of this fact. Do you know the reason behind the huge success of Pokemon Go? Well, it is certainly not Image Recognition! It happened because the game was extremely relevant to real life. Users are more inclined towards video games with action in the real world away from the smartphone or other devices.
Mobile app developers play a huge role in creating a user-friendly and easy navigational app. Thus, for every new aspirant who wishes to develop apps using new technology then, polish your skills.
There are some limitations too. As described above, the whole process requires datasets. There are more objects than humans. There are millions of brands, millions of products, thousands of food items, etc. Is it really possible to store data of all these in the cloud? The storage capacity is the biggest challenge. Another limitation is detecting damaged objects. It is not at all necessary that the objects are in good condition.
If the objects are damaged, it can be problematic for the device to recognize. There are other challenges such as low resolution, the introduction of artifacts because of compression, constant changes to lighting conditions, etc.
There are pros and cons of each technology. It is important to overcome challenges and create something unique. Image recognition can solve multiple problems. It can be a part of some great technology innovations like driverless cars and robots. Many mobile app development agencies are utilizing image recognition technology to develop innovative and engaging apps.