Image Recognition: What is it and How it Works

Social media platforms accumulate billions of images daily. With this, you can expect to reach petabytes or even exabytes of data. It becomes especially true as social media users  grow in number and contribute to an increased number of images uploaded on the said platforms. Consequently, the surge in users and visual content uploaded could prove more challenging for businesses to manage consistently.

How do businesses address these issues?

Image recognition.

It is a byproduct of technological advancements that have altered our world perception. It is among the most approachable applications of machine learning. Solutions based on image recognition technology have already been implemented in various industries, including healthcare and e-commerce.

With every industry relying on image recognition, visual content are no longer limited to social media. They have become critical for businesses too. For instance, companies can boost their productivity and growth by appropriately analyzing and utilizing visual or graphic data.

It would be beneficial and interesting to delve deeper into the world of  image recognition, how it works, and how it can be leveraged to enhance business operations.

Image Recognition: What Is It?

So, what is image recognition?

Image recognition refers to a computer's ability to comprehend what is depicted on an image or video frame and classify it. In a technical sense, it is a simulation of the human brain's recognition processes, in which math functions serve as substitutes for real neural processes.

This branch of computer vision enables the identification of objects, people, entities, and various other variables in images. However, computer vision encompasses a broader group of techniques for collecting, processing, and analyzing data from the real world. Since the data is multidimensional, it generates numerical and symbolic information during decision-making.

Users share a massive amount of data via apps, social networks, and websites in the modern era. Furthermore, the proliferation of smartphones with high-resolution cameras generates an abundance of digital images and videos. As a result, industries leverage a massive amount of digital data to provide better and more innovative services.

Using Image Recognition in Business

Image recognition distinguishes distinct meanings contained within images. When it comes to marketing a digital brand, image recognition can be advantageous. It entails quickly recognizing objects using machine learning algorithms and transforming a database into a necessary building block. It can be applied to various areas, including customer-centric search, marketing and analytics, and social media marketing.

As a result, marketers can now adjust their campaigns by monitoring user expressions and produce more engaging content.

Businesses with the resources to pilot these systems are vying to be first movers in this highly regulated data-driven industry. The question, what is image recognition used for, is no longer a mystery to these industries.

Below are the sectors that have adopted image recognition.

E-commerce Industry

For e-commerce industries, visual data provides a wealth of information. Businesses can now leverage AI and image recognition to boost their content management team's efficiency. Sending personalized content to shoppers is also made more possible.

E-commerce sites can use image recognition to identify and remove inappropriate content such as counterfeit products or images that do not match the product description provided by the seller. This technology can be used to detect nudity and explicit content in images uploaded by customers for review purposes. Detecting bogus logos masquerading as legitimate brands with the help of logo recognition technology is another example. The said technology automatically flags the products, preventing shoppers from being scammed.

Gaming Industry

Gaming is another industry that benefits significantly from image recognition. Platforms can verify players' identities and deny access to underage users using face recognition. Some teens attempt to trick technology and bypass age restrictions on websites by uploading photos of adults on their profiles. These attempts are made unsuccessful thanks to image recognition.

Facial recognition enables close monitoring of patrons and any transactions occurring within the game. Thus, suspicious activity can be detected and stopped immediately.

Additionally, facial recognition helps identify notoriously misbehaving players on the platform. Users who have been determined to be violating in-house rules will automatically be excluded from the game and prohibited from rejoining. The same consequence happens when a player blocks another user.

If face recognition can identify misbehaving users, then it can also do the same with high-value players. These could be frequent users or those who spend a lot of money. They are rewarded by websites with a superior experience, exclusive features, and increased attention. Facial recognition may enable platforms to provide personalized and superior customer support to players who visit the website on a regular basis.

Healthcare Industry

Detecting tumors or signs of stroke, as well as assisting visually impaired individuals, are all examples of applications of image recognition in the healthcare sector. Research shows that the algorithm detects lung cancers with a 97% accuracy using image recognition.

Furthermore, you can use the same concepts in medical imaging to assist doctors with tasks such as cancer detection in x-ray images. Medical industries can use computer vision techniques to assist blind people with daily interactions, similar to a high-tech walking stick.

Face recognition-based solutions aid in the accurate identification of patients and the elimination of incorrect procedures. It also minimizes intermixing patient files or mismatching patient diagnoses.

In healthcare, facial recognition software is the appropriate inventory to deal with any possible patient impersonation. When integrated into a hospital's video surveillance system, it can be an effective tool for spotting flagged or wanted individuals, drug dealers, and other criminals by scanning the patient's face and comparing it to the hospital's database. It assists in establishing the patient's identity and preventing fraud, including those impersonating another person either in an attempt to obtain expensive medical treatment or smuggle drugs.

Security

Image recognition is a broad term that refers to the process of identifying or verifying a person using facial biometric patterns and data in digital images or video frames. The technology collects unique biometric data associated with their face and facial expressions to authenticate a person.

This technology effectively protects personal data and ensures that sensitive data remains inaccessible to the perpetrator even if the phone is stolen. Face recognition is being applied to an ever-growing number of domains, including safety, security, and payments.

The capability of image recognition technology to detect and identify faces is a useful feature. Home security systems are becoming increasingly intelligent and powerful as well.

Photo, Video, and Entertainment

Finally, there is the industry that deals with thousands of photos and hours of video content consumption—entertainment and media.In this scenario, the cataloging of photo stock images is simplified while the process of preventing the publication of prohibited content on digital platforms is automated. Deep learning algorithms aid in identifying fake content generated by other algorithms. Correspondingly, utilizing image moderation services enables the identification of inappropriate images prior to their publication on social media.

How Image Recognition Works?

Artificial intelligence (AI) enables image recognition by identifying, classifying, and distinguishing images. Already in image recognition search, AI and augmented reality work in tandem. You can perform virtual actions such as searching online or identifying products using a real-world image. This is how image recognition works.

The Google and Bing search engines make it possible to search for virtually anything through image recognition. Simply enter the desired image in the search field, and they will display all related results. Additionally, you can enter the URL of a page, and, in the case of Bing, the photo will be taken immediately.

Google Lens is already a much more focused version of Google's image recognition application, and it allows you to take a photo on the spot or upload an existing image.

From there, the possibilities are endless, including identifying the species of a plant, identifying the names of foods, animals, works of art, authors, historical data, and tourist attractions, as well as locating products.

The effectiveness of this technology is contingent upon the classification of images. Classification entails matching patterns to data. Images are two-dimensional matrices of data, and identifying their contents relies on classifying data into a single category from several possibilities. OCR or optical character recognition is a critical example, as it converts images of typed or handwritten text into machine-encoded text.

The primary steps in the image recognition process are data collection, predictive model construction, and image recognition using the predictive model.

It often requires establishing a neural network capable of processing the individual pixels in an image. These networks are fed with as many pre-labeled images as possible to "teach" them to recognize similar images.

How it works:

1. A data set containing images accompanied by their associated labels is required. For instance, a photograph of a dog must be labeled as a photograph of a dog or something else that people and machines can understand.

1. Following that, these images will be fed into and trained on by a Neural Network. Typically, convolutional neural networks are used. Aside from Multi Perceptron layers, these networks contain convolutional and pooling layers (MLP).

1. The system feeds an image that is not included in the training set into the model and obtain predictions.

Conclusion

Indeed, image recognition is critical in the business world, which is why delegating time understanding image recognition and its uses are imperative. It aids in detecting and removing offensive content, explicit material, and counterfeit products.

The capability of machines to comprehend the data captured by cameras to detect inappropriate images is becoming an essential method to promote a safer online community. It pairs well with content moderation services.

For startups and SMEs looking for an expert provider of advanced moderation services, outsourcing is an excellent option.

Chekkee protects your brand channels 24/7 from the risks associated with user-generated content by detecting and removing offensive and inappropriate visual content in real-time. We ensure that videos and images are moderated consistently using the most advanced moderation tools available. We see to it that user posts adhere to your platform's content guidelines.

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Merlene Leano

Director of Marketing