How can a computer, smartphone or surveillance camera identify objects in the pictures or recognize people in the crowd? What technologies are used to create smart solutions that can imitate human brain functions? Today, we are going to talk about the image recognition technology and sophisticated programming innovations it is based on.
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What Does Image Recognition Mean?
The ability to recognize objects, classify them by certain features and turn this information into action is considered to be the main property of living creatures. Numerous complicated processes happen in their brains instantly and, as it seems, easily.
Until recently, computer systems didn’t possess such properties. But the attempts to make machines simulate biological processes and automate tasks performed by natural visual systems facilitated the development of artificial intelligence and neural networks. They formed the foundation for a comprehensive computer vision technology and its integral part — image recognition.
- Image Recognition Software is a highly concentrated solution category in terms of web traffic. Top 3 companies receive 61% (15% more than average solution category) of the online visitors on image recognition software company websites.
- Jun 29, 2021 Image recognition is rapidly being adopted by businesses in a variety of industries, including e-commerce, automotive, healthcare, and gaming. In this article, we will talk about image recognition software and how it helps machines to recognize text in a better way. We will tell you how image recognition is useful and some tools for it.
Image recognition software helps you identify objects, people, places, recordings and actions in images or videos. Such programs use the neural network technology that processes all the pixels that are part of an image. The information detected by photo recognition software can be used for many purposes. Mar 23, 2021 What is Image Recognition Software? Image recognition software, also known as computer vision, allows applications to understand images or videos. With this software, images are taken as an input, and a computer vision algorithm provides an output, such as a label or bounding box.
Computer vision is an interdisciplinary field that aims to analyze digital images or video sequences and make certain decisions, e.g. visual content search or autonomous robot guidance, through the lens of extracted data.
Just like the human brain uses two-thirds of its resources for visual processing, computer vision requires massive amounts of power and encompasses various technologies, hardware and software, including image recognition algorithms, to get its sight right and ensure correct understanding. The algorithms are trained with machine learning models to identify people, objects or certain features in digital images and compare them with the millions of preloaded pictures in the database.
IT giants such as Google, Facebook and Pinterest, as well as numerous other companies, are actively researching the field and investing significantly in the image and facial recognition applications.
According to the survey by MarketsandMarkets, the image recognition market is predicted to grow from $15.95 billion in 2016 to $38.92 billion by 2021, at a CAGR of 19.5% for this period.
Read also: How Artificial Intelligence and Robotics Are Changing Our Lives |
Business Usage of Image Recognition
From a business perspective, programs and gadgets performing visual tasks are applied in numerous domains: for retail processes in the eCommerce industry, for accident avoidance in self-driving cars, for people identification in security systems, etc. Let’s take a look at some industries that gain the most advantages of image processing.
E-Commerce
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Online retailers can be considered major adopters of this technology since their business is based on product search and targeted advertising. eCommerce image recognition is powered by visual search engines and app s that can identify products you are looking for (for instance, you take a photo of a product and the system finds this product in the network and suggests where you can buy it). It also provides instant recommendations on similar products you may like. Thus, you get a virtual showroom in your smartphone.
The ViSenze company — a prominent example of AI solutions providers — offers the following products and services:
- Search by image
- Visual commerce platform
- Automated product tagging
- Visually similar product recommendation
In partnership with Coveo, SaM Solutions delivers relevant customer experiences based on AI-search and recommendation technologies.
Read also: IoT in Retail and E-Commerce: Effective Application Scenarios |
Gaming
Visual technologies empower game developers and designers to create incredibly realistic graphics and build new user experiences for interactive games.
For instance, object identification models can track body movements and identify players of different teams, which helps coordinate actions in the real-world gaming space.
Automotive
The leading global automakers such as Audi, Volvo, Uber and Tesla, in cooperation with IT giants such as Google, are competing to invent the autonomous car, which would be able to drive without human assistance. Though this is still a distant goal, partial driving automation already exists.
This has become possible due to the development of embedded image processing car systems powered by deep learning. Thousands of images of road and traffic conditions are fed into the neural network to train intelligent models. As a result, they can perform the following:
- Detect pedestrians
- Detect obstacles on the road
- Read road signs
- Identify stop lights
- Warn about proximity to sideways and safety barriers
- Warn about changing weather conditions
The more training data is provided, the smarter systems become.
Read also: IoT in Automotive Industry: Self-Driving Cars |
Surveillance and Security Systems
Thousands of street and office cameras are equipped with facial recognition software for ensuring the safety of people and property. This is the foundation of smart cities, where every accident is tracked, and every troublemaker can be easily found using three basic outputs:
- Motion detection
- Face detection
- Face identification
Moreover, image processing is applied frequently in the field of biometrical passwords, i.e. when users unblock gadgets or doors with their faces or with fingerprint identification.
How to Make Image Recognition Software
So, what does it take to build an image recognition app? The app creation for image analysis is not as difficult as it sounds. What you need is to choose an appropriate language that can handle complicated algorithms, combine it with necessary machine learning libraries and frameworks, and design the script.
Read also: Microsoft Azure Cognitive Services: Why Is It a Leader on the Cognitive Computing Market? |
The following is a list of the best programming languages for image processing.
C/C++/C#
C, C++ and C# programming dialects of the C-family are used widely for the creation of artificial intelligence programs. Their native libraries and specifications such as EmguCV, OpenGL and OpenCV have built-in intelligent features for processing pictures and can be utilized for quick development of AI apps. With these languages, you can write the code from scratch as well.
C++ is considered to be the fastest programming language, which is highly important for faster execution of heavy AI algorithms.
Read also: Top 10 Programming Languages and Their Use Cases |
A popular machine learning library TensorFlow is written in low-level C/C++ and is used for real-time image recognition systems.
Advantages:
- A collection of AI libraries and tools
- High speed of execution
- Object-oriented principles useful for organizing data
Disadvantages:
- Not suitable for multiple tasks
- Hard to learn for newbies
Java
One of the most commonly used languages, object-oriented Java has equal power to build simple desktop apps and complex AI-based functionalities. It is appropriate for search algorithms, neural networks and natural language processing (NLP) solutions.
The greatest Java leverage is its native machine learning and image recognition libraries, using which you can create apps from scratch. It is also compatible with open-source libraries, e.g. OpenCV (Open Source Computer Vision Library ). Moreover, Java solutions are platform-agnostic and can run on any platform without recompilation.
Java derives from the C-family, taking a part of its syntax. At the same time, it has lesser tools and is easier to use and debug than, for instance, C++.
Advantages:
- Portability
- Simplicity (easier than C++)
- ML libraries
Disadvantages:
- Slower than C++
- May need dramatic changes on older platforms
- Is still an immature AI language
Read also: Java: Is It the Best Language for Artificial Intelligence? |
Python
Today, Python and image recognition are related concepts. A high-level Python supports functional, procedural and object-oriented styles of programming while having a simple syntax and being portable: it can be used on Windows, Mac OS, Linux and UNIX platforms.
The Python programming language delivers smart capacities that are applicable for NLP solutions, neural networks, identification of pictures and movements. Its compatibility with a range of libraries, such as an open-source ML library TensorFlow, empowers Python developers with smart tools for the creation of complex algorithms.
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Advantages:
- Portability
- A rich collection of libraries and tools
- Easier than Java and C++
- Faster than Java and C++
Disadvantages:
- Not suitable for mobile development
Embedded software development and IoT projects often incorporate Python in their technology stack.
Read also: What Is Extended Reality and What Can We Do with It? |
MATLAB
MATLAB is a programming platform with an array of built-in tools and functions, and a namesake matrix-based language for scientists and engineers involved in computational mathematics.
Since image recognition and matrix calculation are interconnected, MATLAB turns out to be an excellent environment for deep learning and machine learning applications.
Using MATLAB, you can:
- Analyze data
- Build algorithms
- Develop models and apps
- Perform testing
Advantages:
- The matrix is the basic element
- Functionality can be expanded by additional tools
- Usability
Disadvantages:
- Can be slow because it’s an interpreted language
Numerous advanced face recognition programs are written in MATLAB.
Build Intelligent Solutions with Us!
SaM Solutions has considerable experience in developing software with incorporated intelligent elements. Here are some examples of AI-based projects our team took part in:
- Mobile application with a face API service based on Microsoft Cognitive Services, voice and image recognition technologies
- A solution for speech recognition and its transcription into text based on Microsoft Azure
- Computer vision API for cataloging images by tags
- Video indexer for searching pictures in a video by tags
- Service for command recognition that can be used in smart homes
The enormous volume of visual data, in case it is processed properly, creates growth opportunities for many businesses. These are targeted advertising, smart photo libraries, interactive media solutions, security systems, accessibility solutions for the visually impaired, and much more.
Do you want to seize these opportunities? Rely on our specialists in the choice of languages and technologies for implementing your ideas and delivering better services to your customers.
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In 2016, Mark Zuckerberg laid out details at Facebook’s annual developer’s conference about their quest to launch AI that is better at recognizing images than people are. These image processing algorithms could be used for everything from narrating images for the visually impaired to avoiding car accidents to automated image tagging. These are just a few of the nearly-infinite applications of image processing APIs, which fall under the umbrella term computer vision.
Below we delve into some of the best image recognition APIs out there, covering a wide range of different applications and features.
7 Best Image Recognition APIs
Image recognition APIs are part of a larger ecosystem of computer vision. Computer vision can cover everything from facial recognition to semantic segmentation, which differentiates between objects in an image.
Working with a large volume of images ceases to be productive, or even possible, without some sort of image recognition in place. Certain tasks, like detecting similar images or landmark identification, are even next to impossible without advanced AI tools.
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For example, consider GrubHub’s use of image recognition APIs for automating images being added to their platform. The simple task of posting images of food to an app is surprisingly fraught. GrubHub developers express a need for image recognition APIs for everything from detecting explicit content to finding similar images.
For the scope of this article, we’ll be focusing on image processing APIs as there are a lot out there. Some of the image processing APIs can be used for other computer vision applications. They’re still worth a look if you’re developing a different kind of computer vision tool.
1. CloudVision API
Google’s CloudVision API is about as close to a plug-and-play image recognition API as you can get. It’s pre-configured to tackle the most common image recognition tasks, like object recognition or detecting explicit content.
The CloudVision API is also able to take advantage of Google’s extensive data and machine-learning libraries. That makes it ideal for detecting landmarks and identifying objects in images, which are some of the most common uses for the CloudVision API.
It also can access image information in a variety of ways. It can return image descriptions, entity identification, and matching images. It can also be used to identify the predominant color from an image.
The CloudVision API’s most exciting feature is its OCR recognition. The API can detect printed and handwritten text from an image, PDF, or TIFF file. You can use it to generate documentation straight from graphics and hand-written notes. This alone makes it worthy of investigation.
The only real downside to Google’s CloudVision API is that it’s a bit expensive. Prepare to pay if you’re going to be using it extensively.
Google Cloud Vision API correctly identifies a cassette tape, listing most probable web entities. Try the demo here.
2. Amazon Rekognition
Amazon’s Rekognition API is another nearly plug-and-play API. It also handles the common image recognition tasks like object recognition and explicit content detection. It has some other features which make it useful for video processing, however. The Celebrity Recognition feature also makes it useful for apps or websites which display pop culture content.
The Capture Movement feature is one of the first standout features of Recogniktion. The Capture Movement feature tracks an object’s movement through a frame. Although largely useful for video processing, it’s worth having in your API toolkit.
The Detect Text In Image feature is also worthy of mention and likely to be more useful for static image processing. The Rekognition API analyzes images for text, assessing everything from license plate numbers to street names to product names.
Rekognition has a number of payment levels. It does offer a free tier, which makes it noteworthy. Rekognition users can analyze up to 1,000 minutes of video; 5,000 images; and store up to 1,000 faces each month, for the first year.
Amazon Rekognition’s pricing also varies by region. If you’re going to use more than their free service, you can request a quote via the pricing page.
Amazon Rekognition being used to detect text within images.
3. IBM Watson Visual Recognition
IBM’s Watson Visual Recognition API combines an image recognition API with the power of machine learning. Users can build, train, and test custom machine learning models, either in or outside of Watson Studio.
It comes with several pre-trained object detection models. These include the General Model, which provides a classification for thousands of predefined objects. The Explicit Model detects inappropriate content. The Food Model recognizes food objects in images. The Text Model recognizes text, similar to Amazon Rekognition.
IBM Watson recognizes some elements of the banana, as seen in this JSON response. Try the demo here.
4. Microsoft Image Processing API
Microsoft Azure Cloud offers a number of tools as part of their Cognitive Services. It’s nearly a one-stop-shop for any kind of Computer Vision processing you might need.
Microsoft Azure Cloud’s Computer Vision API offers a number of the same image recognition tools as the other APIs on our list. It also offers some innovative other features that make it worthy of inclusion on our list of best image recognition APIs. Image properties definition can assess the dominant hue of an image, and whether or not it’s black-and-white. Image Content Description and Categorization describe an image as a complete sentence as well as categorizing that content.
Microsoft Azure Cloud’s image recognition API is priced according to the region as well as by the number of transactions.
Microsoft Azure Image Processing API correctly identifies “headphones” with a 93% degree of confidence. Try the demo here.
5. Clarifai
Clarifai is another image recognition API that takes advantage of machine learning. Clarifai features 14 pre-built models of computer vision for analyzing visual data. It’s also simple to use. Simply upload your media and Clarifai returns predictions based on the model you’re running.
Clarifai has a number of noteworthy features. Its fashion identification system is one of the most in-depth out there, being able to identify thousands of fashion items and accessories using the Fashion computer model. It also features an extensive food algorithm, being able to analyze over 1,000 food items down to the ingredient level.
Clarifai is also capable of most of the basic computer vision functions mentioned on our list. It can detect explicit content, identify celebrities, and recognize faces. Clarifai can also determine the dominant color of an image.
What working with the Clarifai API looks like in curl.
6. Imagga
Companies using visual recognition and processing APIs often deal in huge volumes of visual media. Imagga API is an automated image tagging and categorization API to help you deal with that quantity of media.
Imagga is categorized as a Digital Asset Management API. It features an asset library, allowing for asset categorization and metadata management. Finding assets in the library is simple thanks to a Search/Filter function.
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It also allows for reporting and analytics. It’s comparable to other digital asset management APIs like Box, Airtable, or Canto Digital Asset Management. Imagga’s the new digital asset management API on the block, though, making it more affordable than a number of the other options out there.
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Imagga identifies a cactus… sort of. Try the Imagga auto-tagging demo here.
7. Filestack Processing API
If you’re processing large amounts of photos, Filestack Processing API is a good tool to have in your toolkit.
Filestack Processing API can be used to store files, compress files, and file conversion. It can also automatically integrate with file-sharing platforms like Google Drive, Dropbox, and Facebook. It can also perform many of the other tasks that the other image processing APIs mentioned on our list, like detecting inappropriate content and character recognition.
Filestack Processing has a few other distinctive features that are worth noting. It can be used to tag videos and detect copyrighted images. It can also be used to size or resize images, crop, resize, compress, or rotate images.
Filestack Processing API is 96% percent sure this is a cactus, and we have to agree. Try the Filter Content demo here.
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Image Recognition APIs: Final Thoughts
As you can see, there are a lot of different image recognition APIs to choose from. A number of them perform many of the same basic image recognition functions. Each one has its own unique capabilities as well, though.
To help you decide which image recognition API is right for you, here’s a short synopsis of the features of the APIs we’ve covered in this article.
- For an extensive library of pre-configured recognition models, and quality handwriting recognition, consider Google Google CloudVision API.
- For image recognition with celebrity recognition or movement capture, consider Amazon Rekognition.
- For powerful machine learning from IBM Watson, and a dedicated Food Model, consider IBM Watson Visual Recognition.
- For similar features plus dominant hue and human-readable content description and categorization, consider Microsoft Image Processing API.
- For image recognition that includes fashion and food identification, consider Clarifai.
- For a more affordable API that focuses on a large quantity of media and digital asset management, and NSFW filters, consider Imagga.
- For OCR & NSFW filtering, plus additional file management features like social upload and image transformation, consider Filestack Processing API.
Considering how visual humans are, and how much visual data we’re surrounded by on any given day, it’s safe to say that image recognition APIs aren’t going anywhere anytime soon. It’s technology’s job to make our jobs more efficient, not create an endless array of new tasks to fill our days with endless busywork.
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Image recognition APIs automate a lot of the tasks around working with visual data and media, so we can focus on building our apps, developing our businesses, and finding outstanding visual content without becoming glorified file clerks.
API | Features |
---|---|
Cloud Vision API | 1. Object Recognition 2. Explicit Content Detection 3. Landmark Detection 4. Object Recognition 5. Return Image Descriptions 6. Entity Identification 7. Image Matching 8. OCR Recognition |
Amazon Rekognition | 1. Object Recognition 2. Explicit Content Detection 3. Celebrity Recognition 4. Motion Capture 5. Detect Text In Image |
IBM Watson Visual Recognition | 1. Compatible with Machine Learning 2. Several Pre-loaded Object Identification Machine Learning Models |
Microsoft Image Recognition API | 1. Face Detection 2. Landmark Detection 3. Celebrity Detection 4. Text Recognition 5. Information Extraction From Documents 6. Image Properties Description 7. Image Content Description and Categorization |
Imagga | 1. Automated Image Tagging 2. Automated Image Categorization 3.Creates Analytics |
Filestack Processing API | 1. Stores Files 2. Compresses Files 3. Converts Files 4. Integrates With File Sharing Services 5. Explicit Content Detection 6. Video Tagging 7. Image Editing |
Clarifai | 1. Automated Image Tagging 2. Face Detection 3. Celebrity Detection 4. Demographic Analysis 5. Moderation |
Would you like your tool added to this list? Please comment below. Our policy is to create an “Honorable Mentions” list for other tools that we do not cover in-depth.