What is image recognition? How to realize image recognition?

In any case, we have to admit that in our current era, technological development has a decisive influence on modern life.

But the mixed news is that technology is changing so fast that we can hardly keep up with it, let alone predict the future. One of the fastest growing, most influential and most attractive technological advancements is image recognition.

What is image recognition?

Image recognition is one of the mechanisms of computer vision, and computer vision is a branch of artificial intelligence.

As we mentioned in the article The difference between AI, machine learning and deep learning, artificial intelligence (also known as AI) is a computer system that can imitate human characteristics and perform tasks that usually require human intelligence.

In order to make AI more convincing, we need so-called "computer vision". According to VentureBeat, computer vision is "the computer acquires, processes and analyzes data mainly from visual cues or thermal sensors, ultrasonics and similar sources.

In short, computer vision enables machines to "see" things-even things that humans cannot see. For example, Carnegie Mellon University in Pittsburgh (USA) is actually working on a computer vision application called "breathing cam". The application is equipped with four cloud-connected cameras, allowing users to monitor and record air pollution, and even trace back to the source of the pollution. Yes, it "sees" the air quality.

However, if we want machines to do things that humans cannot do, we must first enable machines to do what humans can: see and mark objects and creatures. This is the main function of image recognition.

Tensorflow is an open source software library created by Google developers. It defines image recognition as a process in which a computer decomposes an image or video into pixels and recognizes the shape in order to "see" the content of these images and classify them.

For example, stock websites have millions of images uploaded and billions of searches every day. Generally, website builders must add tags and descriptions to each photo they upload in order to match the user’s search terms. By installing an image recognition application, once the image is transmitted to the server, the machine can automatically recognize the person or object in the image. Then, it can automatically describe the image, more specific than human description, thereby optimizing the search engine and improving the user experience.

How to realize image recognition?

At present, deep learning is the technology most likely to enable machines to realize the ability to "see". Simply put, deep learning is a machine learning framework that provides computers with autonomous learning capabilities by imitating the human neuron system. Therefore, the computer can accurately identify the content in the picture without the need to install manually coded software according to instructions-but it requires a lot of data to complete the identification.

Therefore, the whole world is committed to the development of large amounts of data, the most typical examples of which are the ImageNet and PASCAL data sets. After years of hard work, these huge and free data sets contain millions of images, and each image is tagged with keywords related to the content of the image.

1. ImageNet: Created by researchers at Princeton University in 2009, this visualization data set has more than 14 million URL images collected from search engines such as Flickr. During the creation of the dataset, the staff and volunteers annotated the submitted pictures in detail and classified them into about 1,000 object classes.

2. PASCAL: PASCAL was jointly created by universities in European Union countries. Compared with the ImageNet dataset, PASCAL pales in comparison with only 20 object classes and a total of 20,000 training images.

As you may have guessed from the huge difference in the number of classes between the two, PASCAL's classification is more versatile. On the contrary, ImageNet focuses on a key feature of the development of image recognition technology: inter-class differences-the machine can recognize two different types of images containing the same species or objects, so the images are classified into different categories. For example, although the same picture only belongs to the category of "dog" in PASCAL, it may be classified as "Corgi", "Shepherd" or "Pug" in ImageNet.

Why invest in image learning?

It looks like everyone is doing this, doesn't it? Because they are indeed doing this.

In 2012, QualcommConnectedExperiences launched the Vuforia software platform for the first time. The platform uses image recognition technology to provide a large number of AR and VR related functions, allowing mobile application developers to expand their horizons at will.

Facebook started to help blind people "see" photos and images in 2016. By using image recognition, the FacebookIOS application will generate a description for each photo and read it aloud to the user.

Earlier this year, Google-one of the world's most noteworthy artificial intelligence companies launched CloudAutoML-a tool designed to simplify the application of AI in enterprise operations. CloudAutoML first started the image recognition function, allowing Google users to drag in images and teaching the user's system to recognize the images on the Google cloud. Companies such as Disney and UrbanOutfitters have applied it to website searches to make the results more in line with user needs.

However, artificial intelligence applications are not the prerogative of large companies. According to an analysis by Bloomberg chief economist McDonough, since mid-2015, there have been more and more corporate earnings conference calls that mention “AI” or “AI companies”. In fact, 80% of the interviewed companies stated that they use AI applications in production.

Why are billions of dollars invested in this technology? Our guess is that image recognition has great potential.

Image recognition is a very abstract field. However, when applied to specific situations, its potential to change the enterprise is irrefutable. Let us look at several potential applications of image recognition in various industries and business processes:

1. Healthcare: One of the most prominent capabilities of image recognition is to assist in the creation of augmented reality (AR)-a technology that "superimposes computer-generated images on the user's perspective of the real world". If you provide artificial intelligence with AR technology and a data set containing visual cues of diseases, you will have a medical assistant that will never be remembered. With it, doctors can obtain real-time detailed diagnosis advice or medical documents of the patient's wound during the examination.

2. Education: Image recognition allows students with learning difficulties or physical disabilities to obtain the education they need in a form that they can perceive. Computer vision-supported applications can provide text-to-speech and image-to-speech functions to help students with impaired vision or dyslexia to "read" the provided content.

3. Food and beverages: By using image recognition, simple applications on smartphones can obtain visual cues of images on Instagram and Facebook, analyze them and provide real-time data. For example, based on these photos, the app can tell you whether a certain cafe in Singapore is a frequent place for your family and friends, or a place for crazy gatherings. In this way, users can get local customized solutions at a glance, and restaurants can effectively reach target audiences.

4. E-commerce: imagine a user sees something they want to buy on the street, but they can’t find someone to ask where they can buy it, so he takes a picture. Then, the user uploads it to an e-commerce website equipped with image recognition technology. The algorithm itself can "see" pictures, scan millions of alternatives, and recommend an option that looks the same as what the customer is looking for, or at least the closest option. This is exactly the original intention of Savvycom when it created the new AILab in March 2018. Now, our engineers are developing artificial intelligence visual search tools to use large e-commerce data sets with thousands of products to expand the e-commerce experience.

5. Enterprise process management: The advanced image recognition system can also assist in recognition when the company is operating. For example, machines can perform facial recognition, which will replace traditional ID cards to determine whether someone is granted the right to perform a certain task: such as accessing a file storage system, attending a meeting or checking work. However, we have to admit that due to personal emotions, makeup and other factors, "seeing" and "recognizing" faces are much more complicated than recognizing objects. Therefore, Savvycom's goal is to solve this problem as soon as possible in the upcoming project.

What obstacles are facing the development of image recognition technology?

Image recognition is not a new field, but looking at the big picture, it is still in its early stages. Just like any typical growing up teenager, there are also problems in adapting to the real world.

Remember "80% of organizations said they used AI applications in production"? Among these companies that have applied artificial intelligence technology, about 33% said that the biggest obstacle to adopting artificial intelligence technology is instability-immature and unproven. 34% think it is difficult to recruit qualified engineers, and 40% say that the construction of information technology infrastructure hinders the introduction of artificial intelligence technology, and it is easy to adversely affect the company's finances.

Funding is also an important factor. As there are more and more open source software libraries for data flow programming, such as MicrosoftCNTK and Accord.Net, machine learning enthusiasts can conduct research and study at a very low cost. However, not all problems can be solved because not everything is known. In order to achieve product ideas and balance the budget, the company still has a long way to go.

There is a solution that can solve many of the above problems: outsourcing. IT outsourcing companies focus on skills and expertise, and can provide high-end tools and best practices at predictable management costs. In short, they know what they are doing. That is their job.

All in all, image recognition is an early sign of the arrival of the computer vision era. No matter how or what industries it will be applied to, image recognition technology can never develop in isolation. Only by accessing more pictures, real-time data, and spending more time and energy can it be more powerful. Only companies that recognize this and make full use of these connections can succeed in the future.

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