What Is Image Recognition? Applications And Best Software

Thanks to millions of years of evolution, we now have the organs necessary to see and comprehend the visual world around us. For computers, it's still a work in progress.

We can’t just see and comprehend but also imagine. Imagination augments our visual system in many ways. It allows us to recognize specific objects that we haven't seen before.

Have you ever seen a panda with eagle wings? Probably not.

Now that you have read it and formed a mental image, will you recognize it if you ever see one? Most likely, yes.

Machine learning algorithms are limited to the image data sets that are used to train them. If they come across anything outside the training data set, they might act irrationally. This is because machines haven't cracked the imagination part yet.

The field of artificial intelligence, which replicates human brain reflexes, is known as deep learning. One branch, namely computer vision, enables computers to perceive and comprehend the external world like sapiens. These capabilities are powered via image recognition software that enables computers to detect, localize, and categorize image elements for a number of industrial applications. 

Understanding the elements of an image is essential to making the right decisions. Let's look at some examples to see how image recognition helps achieve that. 

Image recognition example 

Some examples of image recognition are Facebook's auto-tagging feature, Google Lens app that translates images or searches elements, eBay's image search and automated image and video organization in Google Photos. By analyzing image parameters, image recognition can help navigate obstacles and automate tasks that need human supervision. 

Another simple example of image recognition is optical character recognition (OCR) software, which identifies printed text and converts non-editable files into formattable documents. Once the OCR scanner has determined the characters in the image, it converts them and stores them in a text file.

It goes without saying that all image recognition techniques can be applied to video feeds. Because, fundamentally, a video consists of a group of pictures that are shown quickly. So, the technique of image recognition can be applied to videos. 

Image recognition vs. computer vision

Image recognition is a sub-category of computer vision. Many use these two terms interchangeably.

Image recognition is a sub-category within computer vision technology that focuses on detecting, categorizing, and restructuring image elements within digital static photographs, videos, and real-world scenarios.  This software is pre-trained on image sets with similar features as that of the test set. The image recognition algorithm analyzes the location of objects, extracts features and submits it to a pooling layer and finally feeds the features to a support vector machine (SVM) to do the final classification. Common applications include facial recognition, biometric authentication, product identification and content moderation. 

Computer vision is a broad field that includes different tools and strategies that is directed to infuse visual capabilities within machines and computing systems. These techniques include object tracking, image synthesis, image segmentation, scene reconstruction, object detection and image processing. The computer vision technique powers several innovations like medical imaging, anatomical organ study, self-assist cars, robotic process automation and industrial automation. The prime goal is to replicate human vision capabilities within computing systems so that they can complete more than one task at a time by acknowledging its visual state and appearance. 

How does image recognition work?

Image recognition is performed with the help of a deep learning algorithm. Like any other machine learning model, an image recognition model needs to be trained on image data. This also means that the model is only as good as the dataset used to train it.

For neural networks to recognize specific elements in images, they must first be trained. An image data set must be collected and labeled or annotated to train the neural networks. In essence, data annotation is informing the model whether the specific element you're training it to recognize is present in the image or not. If it's present, the element’s location information is also included.

Once the model is trained by exposing it to a large number of annotated images, it can check for specific elements and group similar images. The model gets better when exposed to newer datasets.

That's just an overview of the entire image recognition process, and it's easier said than done. Let's dig a little deeper into how a model is trained to make predictions.

Image detection and localization

Simply put, the goal of image recognition is to tell two images apart. The elements that form the image need to be identified to distinguish these images. This is made possible by learning features of images by analyzing them at the pixel level.

The algorithm segregates image elements with the help of bounding boxes to study them more carefully. It comprehends their geographical location, relevance and size in this process and stores the data. Much like how we react when we see an individual. 

Once we see an individual, we subconsciously analyze their appearance. Certain features, including eye color, body type, face shape, hairstyle, posture, and even the kind of clothing they wear, will help us determine who they are. Of course, the human brain processes all this information quite quickly, and it feels almost instantaneous.

In short, to recognize faces (or objects), a system must first learn to recognize their features. This will allow the system to predict what an object is, for example, a dog or a cat.

Supervised, unsupervised and semi-supervised learning

Each image will be labeled with its category, for example, a dog or a cat. This learning technique, in which the algorithm uses labeled datasets to learn the visual characteristics of each category, is called supervised learning.

When the images are unclassified and unlabeled, and the algorithm learns by identifying patterns in the dataset, it’s called unsupervised learning.  Unlike supervised learning algorithms, unsupervised learning algorithms are more adept at performing complicated tasks. However, it's more compute-intensive when compared to supervised learning.

In some cases, algorithms can learn from previous rounds of image classification and use that data to recognize features of the current image. This technique is known as "semi-supervised learning" and has been more accurate and agile than any other technique to date. 

Convolutional Neural Network

Convolutional neural networks were launched in early 2012 and were considered a revelation in image recognition. These networks functioned using a pooling technique to understand and club features. Many variants were released later, like masked R-CNN, You Only Look Once, and Visual transformers. 

Each CNN layer extracts different features of the input image. For instance, the first layer detects basic characteristics such as an image’s vertical and horizontal edges. Each layer trains on the output produced by the previous layer.

In other words, as you go deeper into the CNN, the layers begin to detect complex image features, such as shapes and corners. Each successive layer can recognize characteristics of increasing complexity. This also means that the higher the number of layers, the greater the predictive ability of the neural network.

The final layers of the CNN are capable of detecting complicated features such as faces and buildings. The output layer of the convolutional neural net provides a table containing numerical information. This table depicts the probability that a specific object was recognized in the image.

Tasks of image recognition software

Image recognition software is powered on deep learning, more precisely, artificial neural networks.

Before we discuss the detailed workings of image recognition software, let's examine the five common image recognition tasks: detection, classification, tagging, heuristics, and segmentation.

Detection

The process of locating an object in an image is called detection. Once the object is found, a bounding box is put around it.

For example, consider a picture of a park with dogs, cats, and trees in the background. Detection can involve locating trees in the image, a dog sitting on the grass, or a cat lying down.

Once the object is detected, a bounding box is placed around it. Of course, objects can come in all shapes and sizes. Depending on the complexity of the object, techniques like polygon, semantic, and key point annotation are used for detection.

Classification

It's the process of determining the class or category of an image. An image can only have a single class. In the previous example, if there's a puppy in the background, it can be classified as 'dogs' or simply as dog images. If there are dogs of different breeds or colors, they can also be classified as "dogs".

Tagging

Tagging is similar to classification but aims for better accuracy. It tries to identify multiple objects in an image. Therefore, an image can have one or more tags. For example, an image of a park can have tags like "dogs," "cats," "humans," and "trees."

Heuristic 

The algorithm predicts a "heuristic" for every element within an image, which is a projective score of an element belonging to a specific image category. The heuristic is an estimated measure, usually measured via a distance metric like Euclidean or Minkowski metric. The heuristic is then compared with a "tensor" value, which is calculated by cross multiplication of data properties into a number of grids the image is divided into. The heuristic value sets a predetermined goal for the image recognition algorithm to achieve.

Segmentation

Image segmentation is a detection task that attempts to locate objects in an image to the nearest pixel. It's helpful in situations where precision is critical. Image segmentation is widely used in medical imaging to detect and label image pixels.

Processing an entire image is not always a good idea, as it can contain unnecessary information. The image is segmented into sub-parts, and each part's pixel properties are calculated to understand its relation to the overall image. Other factors are also taken into consideration, like image illumination, color, gradient, and facial vector representations. 

For instance, if you're trying to detect cars in a parking lot and segment them, billboards or signs might not be of much use. This is where partitioning the image into various segments becomes critical. Similar pixels in an image are segmented together and give you a granular understanding of the objects in the image.

Applications of image recognition

Image recognition is widely used in several industrial and consumer applications. It also augments the abilities of narrow AI, which is the level of artificial intelligence we currently have.

Here are some real-world use cases of image recognition:

• Face recognition: Facial recognition has become a standard feature in most smartphones. It's a stable replacement for fingerprint ID and a convenient security feature that lets users unlock their devices without tapping or pressing certain buttons. Face recognition can also aid forensic investigations and control access to sensitive areas in a workplace.
• Medical image analysis: Image recognition has multiple applications in the healthcare field, including detecting brain strokes or tumors by helping doctors examine medical images. Image recognition systems are also used to detect lung cancers with high accuracy.
• Image search: It's easier to search with an image than with words, and so many e-commerce sites and applications are offering customers the ability to search using images. Image search is handy if a customer doesn't know what exactly a product is called.
• Damage assessment: Image recognition can automate car damage assessment, notably reducing the expense evaluation time of a damaged vehicle. Instead of waiting for an assessor to visit, car owners can send pictures of the damaged car to the claims department, which will then be analyzed with the help of image recognition software.
• Marketing: Social media networks generate a significant amount of image data, which can be used to understand customer preferences. Identifying visual trends using image recognition tools is an example. Similarly, marketers use logo recognition to determine how much exposure a brand receives from an influencer marketing campaign.

Best image recognition software in 2024

Image recognition software enables developers to create applications that can comprehend images or videos. The software takes an image as input and provides a label or bounding box as output. Some image recognition tools offer image restoration, object recognition, and scene reconstruction features.

Although most image recognition software is multipurpose, meaning they can recognize different types of images and objects, some have specific applications.

To qualify for inclusion in the image recognition category, a product must:

• Accept image data as input and offer a solution
• Offer a deep learning algorithm specifically designed for image recognition
• Offer image recognition abilities to other applications, services, or processes
• Connect with image data pools to understand a function or solution

* Below are the five leading image recognition software from G2's Spring 2024 Grid® Report. Some reviews may be edited for clarity.

1. Google Cloud Vision API

Google Cloud Vision API offers you pre-written code and functional threads to run your image recognition program. Localize, identify, and classify objects for your business with Google's pre-trained vision API software. With a prior software library, pre-trained weights, and deep learning module, the software can be embedded into any business application concerning facial monitoring, surveillance, sensory tracking, and activity regulation and compliance. 

What users like:

"We are using vision API in a project where we have to know the food's nutrition value, so we get the food name by Image recognition API and then calculate its nutrition as per food contents. It is very easy to integrate it with our application and the api response time is also very fast. The models used by vision API are one of the best models in the Image recognition field."

- Google Cloud Vision API Review, Badal O.

What users dislike:

"Since every product has its own downsides so does this API. One is we cannot use this great product offline other is there are only limited pre-trained models as it has little room for customization. Well cost is pay as you go which i would suggest to give users atleast 2months of free trail instead of 1 month."

- Google's Cloud Vision API, Vritika M.

2. Syte

Syte has been a leader in the image recognition category. With a myriad of visual search and object detection capabilities, this tool is being popularly used by businesses to run their facial monitoring and scanning processes. It embeds smart technology to interpret core visuals and identify the image class and background based on the provided data. 

What users like:

"First and foremost, the standout feature of Syte.ai is the exceptional support and assistance provided by their team. As a Product Owner deeply involved in the implementation process, I found the Syte team to be highly responsive, knowledgeable, and genuinely committed to ensuring the success of our project. They were readily available to address any queries or concerns, providing prompt and effective solutions. We were able to implement the tool under the schedule of a month!

The results so far are promising.. so we are very happy to count on this partnership."

- Syte Review , Rafael N. 

What users dislike:

"There was some difficulty in enabling different accounts to the analytics dashboard. It would be nice to have no restrictions on these logins (different users should be able to access it). It would also be very useful to receive a manual that allows us to understand how to use these dashboards (however, a meeting was organized, where the Syte team explained how they works)."

- Syte Review, Daniel C.

3. Clarifai

Clarifai is a generative AI tool  which provides integrated interfaces for computer vision, large language models (LLM) and audio AI production. The tool supports complete AI production lifecycle like preparation, training, validation, retraining and production.  Clarifai has been a leader in the image recognition category and provides end to end tool to recognize elements within digital images. 

What users like:

"The Clarifai platform is pretty intuitive to utilize and has many models to choose from. It has some of the most cutting-edge models from Open AI and similar organizations. The number of model permutations you can configure and the vast array of the feature sets are pretty cool.

In terms of model tuning and performance, that is pretty straightforward. Also, the capability to run the models with minimum effort in a chained workflow has been pretty useful, especially for internal use cases."

- Clarifai Review, Sam G.

What users dislike:

"The only issue I have encountered is the issue of "model is deploying or scaling up". This sometimes takes a lot of time to get resolved or I need to change the model for my use case."

- Clarifai Review, Hemanth Sai G.

4. Gesture Recognition Kit

"It is mainly more programmable for C++ in which I have most of efficiency because I have learned that from the starting of my learning journey. Most of time I used this for Gaming Development In which I worked for some features of Virtual Reality."

- Gesture Recognition Toolkit Review, Dhruvii B.

What users dislike:

"Gesture Recognition Toolkit has occasional lag and a less smooth implementation process. Customer support response times could be faster. However, I am satisfied with its other features."

- Gesture Recognition Toolkit Review, Civic V.

5. SuperAnnotate

SuperAnnotate is an image recognition and annotation tool which users can operate to label and categorize images. It produces image labels based on the underlying confidence score provided to image elements and adjusts it's accuracy based on human intervention. It's expert-managed data annotation capabilities makes it one of the most accurate and highly preferred tools amongst workforces today. 

What users like:

"While their work quality stays superior as agreed upon by many others in the industry, the best traits are customer support and communication. Anytime I had a question, they were ready to answer and if SuperAnnotate needed information, the questions were clear and to the point. Even the personnel changes are carried out seamlessly and after a complete KT."

- SuperAnnotate Review , Sai Bharadwaj A.

What users dislike:

" While the basic interface is praised for its user-friendliness, some advanced features and functionalities might require a steeper learning curve, especially for less technical users."

- SuperAnnotate Review, Jesus D.

Looking through the visual lens 

Image recognition is a crucial advancement in artificial intelligence that would fuel a successful robotic future. By getting better at identifying objects within images, these tools can be used for pertinent border and security tasks and would be pivotal in maintaining world peace. For other industries, it can spark a great deal of enthusiasm between tech-enthusiasts and scientists that seek constant streams of innovation and automation. 

Learn more about how artificial intelligence is changing the world with these 14 revolutionary industrial applications in 2024.