Image Recognition AI: Algorithms And Applications Machine learning began with humans feeding information to the computer through the usage of keyboards for them to understand and develop certain learned patterns. This process relied heavily on the ability of the human to enter the correct information and help the computer develop its patterns.
This breakthrough does not really require someone to feed the information to the computer or be their eyes so to say. But why is that so?
Because this new technique allows machines to interpret and categorize whatever they see in images or videos. In other words, computers now have their own eyes. Therefore, they work independently with the ability to recognize whatever is around them.
Called “image classification” or “image labeling”. This is a foundational component for the world of vision-based machine learning.
What Is Image Recognition?
Understand this as a computer vision task that works to not only identify but also categorize various elements of image and/or videos through a process of inputting and eventually outputting information.
The models work to interpret images as inputs and then label any matches as output. You’re probably a little confused. So let’s break this down into three easy-to-understand steps.
1) The image recognition model is suitable for images that are labeled as “apple” or “not apple”.
2) The model input can now either be an image or a video frame.
3) The model output will now show the likelihood or “confidence score”. This indicates the presence of that particular input/object within the image.
Image Recognition AI
Due to its multi-faceted nature, image recognition can be into two separate classifications:
1) Single class image recognition
Here the model will predict only one label per image. What this means that no matter the input or the diversity in the image, the machine will assign only a single label.
2) Multiclass recognition
In this type, the machine has the ability to assign several labels to an image. This means that one image can have a couple of individual labels. This will be based on the individual likelihood for each case/group.
Image Recognition Algorithm
The basic structure for image recognition is on the variations available for convolutional neural networks (CNNs). These are networks that provide a foundation for the machine to develop connections and establish patterns.
Image recognition models begin with an encoder. These are blocks of layers that have the ability to learn/understand statistical patterns in the pixels of images that correspond to the label(s) that the machine is trying to match it to or predict.
This encoder shares a connection with a fully connected or dense layer which helps release confidence scores (likelihood scores) for every label that has been used as input. The machine processes the images and makes a prediction based on whether it is a single-class or multi-class recognition.
The accuracy of these predictions is catered to through the usage of accuracy metrics on common datasets. These datasets are pre-made and used based on the particular needs of the user of the image recognition software.
Applications Of Image Recognition
Now that we’ve explored the concept and also dived into the details of its algorithms and their workings. Let’s now look at a few use cases/applications of this technological breakthrough.
Visual Search
Visual search refers to the usage of real-world images to make searches that can yield more accurate and reliable results. It helps the searcher get accurate results. Also, helps the retailer understand the customer’s needs better and suggest them items that directly relate to the themes/styles/behaviors/interests of the consumer.
With the incorporation of a deep learning approach, retailers have the ability to understand the content and context of image searches which in turn helps them to respond with personalized lists that are in line with the direct requests of the consumer.
While this is a rather “infant” project, it is gaining speed quickly amongst worldwide retailers that are understanding the importance of studying particular consumer needs and building on their searches/requirements to provide a personally curated experience that can also contribute to sales eventually.
Image Organization
Who doesn’t like taking a million photos with their phones? Whether it’s a random aesthetic shot or the picture of a loved one. Our phones are full of volumes of content that quite literally screams for an efficient way of organizing all of the content — rather than it being everywhere and anywhere.
With image recognition AI, any form of photo or video of an individual can be efficiently organized into categories that are easily accessible. Also, helps with an improved search and discovery mechanism and eventually seamless content sharing.
This is a feature that many of us have already seen in our smartphones. With our images being categorized according to the places/people in the images without the need for manual tagging. This is the same technology that has driven the deployment of facial recognition into the tagging of images and helped categorize images/videos accordingly.
