Not long ago, artificial intelligence sounded like a science fiction prophecy of a tech future. Today machine learning has become a driving force behind technological advancements used by people on a daily basis. Image recognition is one of the most accessible applications of it, and it’s fueling a visual revolution online.
Мachine learning embedded in consumer websites and applications is changing the way visual data is organized and processed. Visual recognition offers exciting opportunities similar to the ones in science fiction movies that made our imagination run wild.
Image recognition has grown so effective because it uses deep learning. This is a machine learning method designed to resemble the way a human brain functions. That’s how computers are taught to recognize visual elements within an image. By noticing emerging patterns and relying on large databases, machines can make sense of images and formulate relevant categories and tags.
From image organization and classification to face detection, here are five of the top applications of image recognition in the current consumer landscape.
One of the most popular applications of image recognition that we encounter daily is personal photo organization. Who wouldn’t like to better handle a large library of photo memories according to visual topics, from specific objects to broad landscapes?
Image recognition is empowering the user experience of photo organization apps. Besides offering a photo storage, apps want to go a step further by giving people better search and discovery functions. They can do that with the automated image organization capabilities provided by machine learning. The image recognition API integrated in the apps categorizes images on the basis of identified patterns and groups them thematically.
Take Eden Photos, a Mac app for photo organization, as an example. It uses Imagga’s image recognition to offer its users image tags, automatic keywording of photos, and auto-categorization on the basis of visual topics. Users can sync their photos’ metadata on all devices and get keyword search in the native Photos app on their iPhones too.
Telecoms are another set of companies that integrate image recognition to improve their users’ experience. They add value to their services by offering image organization and classification for photo libraries, which helps them attract and retain their customers. On the customer side, user experience is improved by allowing people to categorize and order their photo memories.
An illustration of this application is Imagga’s solution for Swisscom. The Swiss telecom needed an efficient and secure way to organize users’ photos for its myCloud online service. With Imagga’s image recognition API installed on premise, Swisscom now offers its customers a safe feature that organizes and categorizes their visual data.
A powerful commercial use of image recognition can be seen in the field of stock photography and video. Stock websites provide platforms where photographers and videomakers can sell their content. Contributors need a way to tag large amounts of visual material, which is time-consuming and tedious. In the same time, without proper keyword attribution, their content cannot be indexed - and thus cannot be discovered by buyers.
Image recognition is thus crucial for stock websites. It’s fueling billions of searches daily in stock websites. It provides the tools to make visual content discoverable by users via search. In the same time, image recognition is a huge relief for stock contributors. They get automatic keyword suggestions, which save them a ton of time and efforts. Image recognition can also give them creative ideas how to tag their content more successfully and comprehensively.
Keywording software tools like Qhero have integrated with Imagga’s image recognition AI to help stock contributors describe and tag their content with ease. Such tools analyze visual assets and propose relevant keywords. This reduces the time needed by photographers for processing of visual material. It makes manual keywording a thing of the past by suggesting the most appropriate words that describe an image.
A range of different businesses possess huge databases with visuals which is difficult to manage and make use of. Since they may not have an effective method to make sense of all the visual data, it might end up uncategorized and useless.
If a visual database does not contain metadata about the images, categorizing it is a huge hassle. Classification of images through machine learning is a key solution for this. With image recognition, companies can easily organize and categorize their database because it allows for automatic classification of images in large quantities. This helps them monetize their visual content without investing countless hours for manual sorting and tagging.
The best part about automated image classification is that it allows for custom training on top of the general image recognition API. This means that businesses can provide custom categories, which the AI is trained to recognize and use. Our case study on Tavisca is a good example of using custom classifiers in practice and automating the process of hotel photos categorization.
Visual recognition on social media is already a fact. Facebook released its facial recognition app Moments, and has been using facial recognition for tagging people on users’ photos for a while.
While face recognition remains a sensitive ground, Facebook hasn’t shied away from integrating it in users’ experience on the social media. Whenever users upload a photo, Facebook is able to recognize objects and scenes in it before people enter a description. The computer vision can distinguish objects, facial expressions, food, natural landscapes and sports, among others. Besides tagging of people on photos, image recognition is used to translate visual content for blind users and to identify inappropriate or offensive images.
Image recognition is applied in other ways on social networks too. For example, the SmartHash iOs app employs Imagga’s API to offer its users an easy tool for automatically creating hashtags for their photos. This allows people to successfully share their images online without the need to research and brainstorm hashtags.
Photo recognition has also been embraced by other image-centric services online. Google Photos and Apple’s Photos app cluster photos on the basis of events and places, plus offer face detection. The application of image recognition significantly enhances users’ experience. It helps them organize their photos in meaningful series. They can easily exchange, say, travel photos with friends who were a part of the same trip.
The applications of image recognition are not limited to consumer services only. Advertising and marketing agencies are already exploring its potential for creative and interactive campaigns. It opens new opportunities for learning more about target audiences and serving them with impressive branded content.
Social intelligence today is largely based on social listening. It involves following conversations on social media to learn more about prospects. But today, this knowledge can be gathered from visuals shared online with much higher efficiency. In a sea of abundant and often irrelevant visual content, extracting useful information is possible only through machine learning - or ‘visual listening.’ For example, image recognition can identify visual brand mentions and expression of emotion towards a brand, as well as logo and other brand data that would be otherwise undiscoverable. On the basis of collected information from analyzing images, marketers can better target their campaigns by using customization and personalization.
Besides valuable information about potential customers, image recognition can be used for crafting creative content that engages people and helps build their relationships with brands. To illustrate this: Imagga’s image recognition API was used in a KIA marketing project to create an interactive campaign. By profiling of participants’ image content online, each person is assigned to a different lifestyle group. Then they are matched to the right car that best fits their style among the 36 different car styles offered by KIA.
Image recognition holds potential for a wide array of uses and industries, so these five examples are certainly not all-encompassing. They do illustrate, though, the diversity of applications that machine learning can offer to businesses that work with large libraries of visual content.
What is your business experience with image recognition?
by Ralitsa Golemanova
We are excited to announce that starting today we are partnering with Blitline, one of the best and most lightweight and easy to integrate image processing services in the cloud!
Our cooperation starts with the opportunity for Blitline users to take advantage of our smart-cropping, as applied in our cropping API and cropping tool Cropp.me. The guys at Blitline have produced a very easy to understand example why Imagga’s smart cropping is better than just center cropping:
Imagga Smart Cropping as explained by Blitline
Funny enough, the idea for the cooperation came a month ago from an existing user of Blitline who approached us and asked if we can provide them with the same seamless integration that Blitline offers for regular cropping, but with our smart cropping, or ideally – can we offer it via Blitline? No sooner said than done! We got in contact and things simply worked out.
Now Blitline users who subscribe for the special Blitline + Imagga plan have access to the imagga_smart_crop function for their image processing pipeline. The pricing sticks to the pay-as-you-go model (per cropped image) plus a very low monthly subscription fee.
Blitline + Imagga Pricing Plan
We do believe this is going to be a great example of co-opetition!
by Chris Georgiev