Why is image recognition a key function of AI?

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Image recognition is a computer vision technique that allows machines to interpret and categorize what they “see” in images or videos. Often referred to as “image classification” or “image labeling”, this core task is a foundational component in solving many computer vision-based machine learning problems.

But how does image recognition actually work? What are the different approaches, what are its potential benefits and limitations, and how might you use it in your business?

What is image recognition?

Image recognition is a computer vision task that works to identify and categorize various elements of images and/or videos. Image recognition models are trained to take an image as input and output one or more labels describing the image. The set of possible output labels are referred to as target classes. Along with a predicted class, image recognition models may also output a confidence score related to how certain the model is that an image belongs to a class.

For instance, if you wanted to build an image recognition model that automatically determined whether or not a dog was in a given image, the pipeline would, broadly speaking, look like this:

  • Image recognition model trained on images that have been labeled as “dog” or “not dog”
  • Model input: Image or video frame
  • Model output: Class name (i.e. dog) with a confidence score that indicates the likelihood of that image containing that class of object.

 

Modes and types of image recognition

Image recognition is a broad and wide-ranging computer vision task that’s related to the more general problem of pattern recognition. As such, there are a number of key distinctions that need to be made when considering what solution is best for the problem you’re facing.

Broadly speaking, we can break image recognition into two separate problems: single and multiclass recognition. In single class image recognition, models predict only one label per image. If you’re training a dog or cat recognition model, a picture with a dog and a cat will still only be assigned a single label. In cases where only two classes are involved (dog; no dog), we refer to these models as binary classifiers.

Multiclass recognition models can assign several labels to an image. An image with a cat and a dog can have one label for each. Multiclass models typically output a confidence score for each possible class, describing the probability that the image belongs to that class.

While there are a number of traditional statistical approaches to image recognition (linear classifiers, Bayesian classification, support vector machines, decision trees, etc.), this guide will focus on image recognition techniques that employ neural networks, as those have become the state-of-the-art approaches to image recognition.

Why is image recognition important?

Image recognition is one of the most foundational and widely-applicable computer vision tasks.

Recognizing image patterns and extracting features is a building block of other, more complex computer vision techniques (i.e. object detection, image segmentation, etc.), but it also has numerous standalone applications that make it an essential machine learning task.

Image recognition’s broad and highly-generalizable functionality can enable a number of transformative user experiences, including but not limited to:

  • Automated image organization
  • User-generated content moderation
  • Enhanced visual search
  • Automated photo and video tagging
  • Interactive marketing/Creative campaigns

 

Of course, this isn’t an exhaustive list, but it includes some of the primary ways in which image recognition is shaping our future.

 

 

 

 

We as humans can easily distinguish places, objects, and people from images, but computers traditionally face a tough time comprehending these images. Thanks to the new image recognition technology, now we have specialized software and applications that can decipher visual information. We often use the terms “Computer vision” and “Image recognition” interchangeably, however, there is a slight difference between these two terms. Instructing computers to understand and interpret visual information, and take actions based on these insights is known as computer vision. Computer vision is a broad field that uses deep learning to perform tasks such as image processing, image classification, object detection, object segmentation, image colorization, image reconstruction, and image synthesis. On the other hand, image recognition is a subfield of computer vision that interprets images to assist the decision-making process. Image recognition is the final stage of image processing which is one of the most important computer vision tasks.

Image recognition without Artificial Intelligence (AI) seems paradoxical. An efficacious AI image recognition software not only decodes images, but it also has a predictive ability. Software and applications that are trained for interpreting images are smart enough to identify places, people, handwriting, objects, and actions in the images or videos. The essence of artificial intelligence is to employ an abundance of data to make informed decisions. Image recognition is a vital element of artificial intelligence that is getting prevalent with every passing day. According to a report published by Zion Market Research, it is expected that the image recognition market will reach 39.87 billion US dollars by 2025. In this article, our primary focus will be on how artificial intelligence is used for image recognition.

 

 

A lot of technological breakthroughs were promised to us, till the year 2020. Although much has not been achieved as claimed, yet the advancement and implementation of revolutionary technologies in the realm of AI (Artificial Intelligence), Big Data, and Machine Learning has contributed immensely to refining Image Recognition technology further.

The dawn of driverless cars, impressive facial-recognition mechanics already implemented in various countries, and faster detection of objects in real-time with accuracy – all were made possible through image recognition algorithms, powered by machine learning.

One can easily predict the Image recognition market to be thriving in the coming years, at this point. The statistics estimate the IR market to reach around 3.8 billion USD by the year 2021. This is a massive hike from a mere 15.9 billion in 2016, nearly 100% increase, according to data by Markets and Markets research.

Image recognition technology has embedded seamlessly in the areas of e-commerce, content sharing (helping in moderating offensive visual content), security, healthcare, and automotive – Let’s look at the image recognition applications that software and app development companies and as a whole are working to pave the way towards a futuristic present, and near-future.

 

 

Image recognition alone can be a very abstract field. But when put into contexts, its potential to transform businesses is indisputable. Let’s look at several potential image recognition application in various industries and business processes:

  1. Healthcare: One of the most prominent Image Recognition ability is assisting the creation of Augmented Reality (AR) – a technology that “superimposes a computer-generated image on a user’s view of the real world”. Giving an AI the AR technology and a database contains visual cue of diseases or illnesses and you have yourself a medical assistant who never forget. With it, doctors then can get real-time, detailed diagnostic suggestions projected on the patient’s wounds or the medical documents during examinations.
  2. Education: Image recognition can allow students with learning difficulties and disabilities to obtain the education they need – in a form they can perceive. Apps powered by computer vision can offer text-to-speech and image-to-speech which assist students with impaired vision or dyslexia to ‘read’ the content provided.
  3. Food and Beverage: By employing image recognition, a simple app on smartphones can caught visual cues in Instagram and Facebook uploaded images, analyze them and offer live data. For example, based on the photos, the app could tell you whether a cafe in Singapore is frequently visited by families and friends, or it’s a wild place to party. This way, customers receive local customized proposal at-a-glance while restaurants can effectively reach out to its targeted audience.
  4. E-commerce: Imagen a customer seeing something they would like to buy on the street. They have nobody to ask where to get it, so they snap a picture. Then, the customer uploads it to an E-commerce site that equipped with image recognition technology. The algorithm itself can ‘see’ the picture, scan through millions of options available and recommend the one that looks identical – or at least, the closest – to what the customer is looking for. This is exactly what Savvycom has in mind when founded the new A.I. lab back in March 2018. Now, our engineers are currently developing an Artificial Intelligence Visual Search tool to utilize the big E-commerce database of thousands of products and amplify the e-commerce experience .
  5. Business Process Management: A more advanced image recognition system can also assist the identification process during business operation. For example, the machine can provide Face ID identification, which will replace the traditional ID cards used to determine whether a person is granted the right to conduct a certain task: access to document storages, attend meetings or simply check in to work. However, we acknowledge that to ‘see’ and ‘recognize’ a human face is much more complex than identifying an object due to emotion illustration and makeup alteration ability. Therefore, Savvycom is aiming to tackle this area as soon as possible with upcoming projects.

 

To sum up, image recognition is the early sign of a future of computer vision. No matter how it will be approached or what industries it will be applied on, image recognition will never be achieved alone. It can only be made stronger by access to more pictures, real-time data, time and effort. The businesses that realize this, make the most of these connections and prepare head-on are the ones that shaped for success.

 

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