User Cases for Image Processing

Glance

The majority of computer vision business applications entail determining what is actually inside an image data collection. And there is a lot of work to be done because visual information is so rich information. However, rather than replacing humans, computer vision capabilities are most typically used to assist humans. To put it another way, computer vision initiatives are typically concerned with developing a workflow in which humans tackle the more complex, creative, or distinctly human activities while machines handle the rest. But what is the ultimate goal here? What are the projects genuinely for? How can we think about them in-depth? While you can categorise them by industry or the annotation tools themselves, it's probably more interesting to categorise them topically which again requires the image dataset collection. The great majority of image processing activities can be divided into four categories:

• User ingenuity
• Social interaction
• Overall effectiveness
• Safety

Let us begin with creativity.

Creativity

We frequently believe that the goals of commercial computer vision are to help people do more things or do them faster/more effectively. However, in some circumstances, corporations are more interested in inspiring clients or stimulating their imagination. On a practical level, Shutterstock helps customers find the proper images in their massive database, but they also propose specific types of adjustments based on what's in the photo that a consumer selects. This is because people want to do different things with portraiture than they do with landscapes. While we might state that image search has a use case, it's probably best to focus on what searches are for because knowing the content of a picture allows them to be more particular with what they help users do. An adjacent use case makes use of user behavior to identify intent while also inspiring inventiveness. People who pin a lot of kitchen pictures on Pinterest, for example, may be considering a redesign. Lowe's has been experimenting with ways to make a person's entire page of pins, match it against Lowe's catalogue to discover related things, and then visually assemble all of these parts. The business goal is to sell an entirely new kitchen, but its success is tied to how Lowe's helps people fantasies and make those fantasies a reality. Caption: Computer vision allows you to recognize objects, user posts on social media and find similar items in your catalogue; here, Lowe's uses Microsoft HoloLens to assist with the assembly of a kitchen. Continuing with Pinterest, their "Shop the Outfit" feature might be classified as both creative and social—people want to look beautiful, so they click on a piece of clothing and find several versions of it. The ability of computers to detect similarities across several axes can fuel a wide range of recommendations. That is models that can recognize what makes a blue high-top a blue high-top can utilize that information to recommend visually similar products. The inverse of resemblance is unusualness; for example, Edvard Munch's "The Scream" is unlike anything that came before it but foreshadowed what was to come. This is the capacity to select noteworthy images or novel styles in the commercial realm.

Something unique frequently appears in unexpected places on a product map or visual space, which may provide fresh and essential information about a client or even begin to uncover trends as those unusual goods become more popular or less unusual over time.

Connection

Because humans are social beings, many projects that appear to be about search/retrieval are really about how we interact with others. This is most visible in Facebook's facial recognition, which discovers and recommends photographs that include friends and relatives. Meanwhile, Apple employs high-level computer models to assist in the search for, say, a dog in your images. Even if you haven't labelled any of the photographs, as you would on Facebook, the model will assist you in finding your favorite canines in your albums. Now, because sharing is at the heart of why consumers search for photos, you want to make sharing a visible and simple aspect of the product design. You may also discover the characteristics of the photographs that users share individually and collectively over time. That is, you can incorporate feedback loops that incorporate user behaviors into AI training dataset, allowing your systems to become smarter over time. In a similar vein, Trulia logs how long people spend looking at various photos of homes for sale in their app, which they use to intuit what a user likes as well as to understand long-term themes across users, down to the level of which enamels and finishes correlate the best with attention and house sales. This is an especially advantageous situation: individual user activities enhance data at a higher, more general level over a longer period. Monitoring what's inside photos also allows businesses to identify products on social media. 

While text dataset analytics may tell you how people are referring to your products, vision models can tell you how frequently things appear on Instagram or any other platform. Understanding a person's fashion and car preferences might also help you target them for related products. You can also match people to places—for example, if you're visiting a new city, where do people who look like you go? As you might expect, many computer vision applications necessitate rigorous ethical consideration of what they do to privacy and social segregation. Caption: If you're a brand, the emotion in the bottom right corner is preferable to the emotion in the bottom left corner. Finally, there are several companies, such as Affective, that strive to discern emotions in photographs and videos. How many people are smiling or frowning in a retail chain? Consider video chat with a customer support representative. While an agent answers to a customer's specific needs, computer vision technologies can look across conversations to understand how much aggravation and relief clients are feeling. Understanding how customers feel in stores or on helplines may assist brands in connecting with their customers and, ultimately, providing better products and experiences.

Efficiency

The simplest business applications for computer vision come from making people more efficient; rather than fixing issues, simply show people where to look. In content moderation, for example, you can train a model to detect objectionable photos without having to subject a group of human content reviewers to the more upsetting items that are posted on websites. Many medical/healthcare computer vision applications are also motivated by efficiency. The intention is not to replace diagnosticians, but rather to direct their efforts. Instead of showing the specialists hundreds of radiological scans in which nothing is out of the ordinary, show them only the photos that the model considers troublesome or in which the algorithm isn't very sure. If there are water limitation provisions in place, but you see a lot of emerald green lawns for Beverly Hills houses, you can bet there are violations. A machine can churn through acres upon acres of satellite photographs much faster than a human can—or go door-to-door. However, models such as the one developed by Omni Earth can be used to discern between a pond and a pool in satellite images. Aerial and satellite images have a range of applications, the most widespread of which are associated with deforestation and urbanization. The MIT Media Lab has sought to identify safer and less safe urban areas, as well as to understand what causes cities to grow. Similarly, using aerial images to detect logging roads that are a forerunner to logging in the rainforest greatly improves enforcement efficiency. Perhaps that's too green for a drought?

Safety

In some circumstances, such as when dealing with a drought, surveillance serves the public good. Most people believe the same is true with facial recognition at security checkpoints. But when does this become excessively intrusive? What about when we're just walking down the street, for example? How about using facial recognition to get toilet paper in a public restroom?

This is a Black Mirror/Minority Report scenario in which facial recognition is used to label people who have done nothing wrong. While there is an effort being done to keep people private while cameras are present—face paint and clothing that might eventually lead to some very wonderful fashion statements in addition to privacy—this is a rising question that we will hear about more and more in the coming years.

Finishing up

The availability of GPUs, the capacity to annotate and score images at scale, and the consistent improvement of models have all combined to make computer vision significantly more viable than it has ever been. That’s why we at Global Technology Solutions are providing image datasets and image data collection services for your AI/ML models.

GTS gives the quality approves datasets to it's clients along with Data Annotation, Audio Transcription and OCR Datasets collection services. Choose with you project needs and get the time efficient, all managed datasets for your business.