Image annotation is crucial in computer vision, the field that enables computers to “see” and “understand” visual information just like humans.
Excellent artificial intelligence (AI) applications include self-driving cars, tumor detection, and uncrewed aerial aircraft. Without image annotation, most of these computer vision applications would be impossible. To build computer vision models, annotation, or annotation of images, is a crucial first step. Valuable machine learning and image recognition approaches rely on datasets.
Image annotation is the process of adding a layer of metadata to an image. It’s a way for people to describe what they see in an image, and that information can be used for various purposes. For example, it can help identify objects in an image or provide more context about them. It can also provide helpful information on how those objects relate to each other spatially or temporally.
Image annotation tools allow you to create annotations manually or through machine learning algorithms (MLAs). The most popular MLA method currently used is called deep learning, which uses artificial neural networks (ANNs) to identify features within images and generate text descriptions based on those features.
Two common annotated image datasets are Google’s OID (Open Images Database) collection and Microsoft’s COCO Collection (Common Objects in Context), which each contain 2.5 million annotated instances in 328k images.
How does Image Annotation work?
Images can be annotated using any open source or freeware data annotation tool. However, the most well-known open-source image annotation tool is the Computer Vision Annotation Tool (CVAT).
A thorough grasp of the type of data being annotated and the job at hand is necessary to select the appropriate annotation tool.
You should pay close attention to:
- The data’s delivery method
- The necessary type of annotation
- The file type that annotations should be kept in
Several technologies can be utilized for annotations due to the enormous range in picture annotation jobs and storage formats. From basic annotations on open-source platforms like CVAT and LabelImg to complex annotations on large-scale data using technologies like V7.
Additionally, annotating can be carried out on an individual or group level, or it can be contracted out to independent contractors or businesses that provide annotating services.
An overview of how to begin annotating images is provided here.
1. Source your raw image or video data
This is the first step in any project, and it’s essential to make sure that you’re using the right tools. When working with image data, there are two main things you need to keep in mind:
- The file format of your image or video – whether it’s jpeg or tiff; RAW (DNG, CR2) or JPEG.
- Whether you’re working with images from a camera or video clips from a mobile device (e.g., iPhone/Android), there are many different types of cameras out there, each with its proprietary file formats. If you want to import all kinds of files into one place and annotate them, then start by importing only those formats that work well together (e.g., jpeg stills + h264 videos).
2. Find out what label types you should use
The type of task being used to train the algorithm has a direct bearing on the kind of annotation that should be used. For example, when an algorithm is being trained to classify images, the labels take the form of numerical representations of the various classes. On the other hand, semantic masks and border-box coordinates would be used as annotations if the system were learning image segmentation or object detection.
3. Create a class for each object you want to label
The next step is to create a class for each object you want to label. Each class should be unique and represent an object with distinct characteristics in your image. For example, if you’re annotating a picture of a cat, then one class could be called “catFace” or “catHead.” Similarly, if your image has two people in it, then one class could be labeled “Person1″and the other would be labeled “Person2″.
To do this correctly (and avoid making mistakes), we recommend using an image editor such as GIMP or Photoshop to create additional layers for each separate object you want to label separately on top of our original photo so that when we export these images later on they won’t get mixed up with other objects from other photos.
4. Annotate with the right tools
The right tool for the job is imperative regarding image annotation. Some services support both text and image annotation, or just audio, or just video—the possibilities are endless. Using a service that works with your preferred communication medium is important.
There are also tools available for specific data types, so you should choose one that supports what you have in mind. For example: if you’re annotating time series data (i.e., a series of events over time), you’ll want a tool specifically designed for this purpose; if there isn’t such a tool on the market yet, then consider building one yourself!
5. Version your dataset and export it
Once you’ve annotated the images, you can use version control to manage your data. This involves creating a separate file for each dataset version, including a timestamp in its filename. Then, when importing data into another program or analysis tool, there will be no ambiguity about which version is being used.
For example, we might call our first image annotation file “ImageAnnotated_V2”, followed by “ImageAnnotated_V3” when we make changes, and so on. Then, after exporting our final version of the dataset using this naming scheme (and saving it as a .csv file), it’ll be easy enough to import back into Image Annotation later if needed.
Want to automate repetitive manual tasks? Check our Nanonets workflow-based document processing software. Extract data from invoices, identity cards or any document on autopilot!
Tasks that need annotated data
Here, we’ll take a look at the various computer vision tasks that necessitate the use of annotated image data.
Image classification is a task in machine learning where you have a set of images and labels for each image. The goal is to train a machine learning algorithm to recognize objects in images.
You need annotated data for image classification because it’s hard for machines to learn how to classify images without knowing what the correct labels are. It would be like going blindfolded into a room with 100 objects, picking up one at random, and trying to guess what it was — you’d do much better if someone showed you the answers beforehand.
Object detection & recognition
Object detection is the task of finding specific objects in an image, while object recognition involves identifying those objects. Finding a thing that you have not seen before is known as novel detection, while recognizing an object that you have seen previously is known as familiar detection.
Object detection can be further divided into bounding box estimation (which finds all the pixels that belong to one object) and class-specific localization (which determines which pixel belongs to which class). Specific tasks include:
- Identifying objects in images.
- Estimating their location.
- Estimating their size.
Image segmentation is the process of splitting an image into multiple segments. This can be done to isolate different objects in the image or to isolate a particular object from its background. Image segmentation is used in many industries and applications, including computer vision and art history.
Image segmentation has several benefits over manual editing: it’s faster and more accurate than hand-drawn outlines; it doesn’t require additional training time; you can use one set of guidelines for multiple images with slightly different lighting conditions; automated algorithms don’t make mistakes as quickly as humans do (and when they do make mistakes, they’re easier to fix).
Semantic segmentation is the process of labeling each pixel in an image with a class label. This might seem similar to classification, but there is an important distinction: classification assigns a single label (or category) to an entire image; semantic segmentation gives multiple labels (or categories) to individual pixels within the image.
Semantic segmentation is a type of edge detection that identifies spatial boundaries between objects in an image. This helps computers better understand what they’re looking at, allowing them to categorize new images and videos better as they come across them in the future. It’s also used for object tracking — identifying where specific objects are located within a scene over time — and action recognition — remembering actions performed by people or animals in photos or videos.
Instance segmentation is a type of segmentation that involves identifying the boundaries between objects in an image. It differs from other segmentation types in that it requires you to determine where each object begins and ends, rather than simply assigning a single label to each region. For example, if you were given an image with multiple people standing next to their cars at a parking lot exit, instance segmentation would be used to determine which car belonged to which person and vice versa.
Instances are often used as the input features for classification models because they contain more visual information than standard RGB images. Additionally, they can be processed easily since they only require grouping into sets based on their common properties (i.e., colors) rather than performing optical flow techniques for motion detection.
Panoptic segmentation is a technique that allows you to see the data from multiple perspectives, which can be helpful for tasks such as image classification, object detection and recognition, and semantic segmentation. Panoptic segmentation is different from traditional deep learning approaches in that it does not require training on the entire dataset before performing a task. Instead, panoptic segmentation uses an algorithm to identify which parts of an image are important enough to use when deciding what information is being collected by each pixel in the image sensor.
Want to use robotic process automation? Check out Nanonets workflow-based document processing software. No code. No hassle platform.
Business Image Annotation Solution
Business image annotation is a specialized service. It requires specialized knowledge and experience. It also requires special equipment to perform the annotation. Therefore, you should outsource this task to a business image annotation partner.
Viso Suite, a computer vision platform, has a CVAT-based image annotation environment as part of its core functionality. The Suite is built for the cloud and can be accessed from any web browser. The Viso Suite is a comprehensive tool for professional teams to annotate images and videos. Collaborative video data collection, image annotation, AI model training and management, code-free application development, and massive computer vision infrastructure system operations are all possible.
Through the use of no-code and low-code technologies, Viso can speed up the otherwise slow integration process across the board in the application development lifecycle.
How long does Image Annotation take?
Timing for an annotation relies heavily on the quantity of data needed and the intricacy of the annotation itself. For example, annotations that contain only a few items from a few different classes can be processed far more quickly than those that have objects from thousands of classes.
Annotations that only need the image itself annotated can be completed more quickly than ones that involve pinpointing several objects and key points.
If you work with invoices, and receipts or worry about ID verification, check out Nanonets online OCR or PDF text extractor to extract text from PDF documents for free. Click below to learn more about Nanonets Enterprise Automation Solution.
How to find quality image data?
It is challenging to gather high-quality annotated data.
Annotations must be built from raw acquired data if data of a certain kind is not freely available. This usually entails a set of tests to rule out any possibility of error or taint in the processed data.
The quality of image data is dependent on the following parameters:
- Number of annotated images: The more annotated images you have, the better. In addition, the larger your dataset is, the more likely it will be to capture diverse conditions and scenarios that can be used for training.
- Distribution of annotated images: A uniform distribution among various classes isn’t necessarily desirable because it limits the variety available in your data set and, therefore, its utility. You’ll want a lot of examples from each class so you can train a model that performs well under all circumstances (even if they’re rare).
- Diversity in annotators: Annotators who know what they’re doing can provide high-quality annotations with little error; one bad apple will ruin your whole batch! In addition, having multiple annotators ensures redundancy and helps ensure consistency across different groups or countries where there may be variations in terminology or conventions across regions.
Here are a few ways to obtain quality image data.
When it comes to image data, there are two main types: open and closed. Open datasets are freely available for download online, with no restrictions or licensing agreements. Closed datasets, on the other hand, can only be used after applying for a license and paying a fee—and even then, may require additional paperwork from the user before being given access.
Some examples of open datasets include Flickr and Wikimedia Commons (both are collections of photos contributed by people all over the world). In contrast, measures of closed datasets include commercial satellite imagery sold by companies like DigitalGlobe or Airbus Defence & Space (these companies offer high-resolution photos but require extensive contracts).
Scrape web data
Web scraping is the process of searching the internet for specific types of photos using a script that automatically does many searches and downloads the results.
The data obtained by online scraping is usually in a very raw state and requires extensive cleaning before any algorithm or annotation can be conducted, yet it is easily accessible and quick to collect. For example, using scraping, we can assemble photos that are already tagged as belonging to a specific category or subject area based on the query we provide.
Classification, which only needs a single tag for each image, is greatly facilitated by this annotation.
Self annotated data
Another type of data is self-annotated. In this case, the owner of the data has manually labeled it with their labels. For example, you may want to annotate images of cars and trucks with their current model year. You can scrap images from manufacturer websites and match them with your dataset using a tool like Microsoft Cognitive Services.
This type of annotation is more reliable than crowdsourced labeling because humans are less likely to mislabel or make mistakes when they’re annotating their data than when they are labeling someone else’s data. However, it also costs more—you have spent money on human labor for these annotations.
Want to automate repetitive manual tasks? Save Time, Effort & Money while enhancing efficiency!
Types of Image Annotation
Image annotation is a process of adding information to an image. Many types of annotations can be applied to an image, such as text annotations, handwritten notes, geotags, etc. Below we will discuss some of the most common types of annotated images:
1. Image Classification
Image classification is a process of assigning a class label to an image. An image classifier is a machine learning model that learns to classify images into different categories. The classifier is trained on a set of labeled images and is used to classify new images.
Classification has two types: supervised and unsupervised. Supervised classification uses training data with labels, while unsupervised does not use labeled data but instead learns on its own from unlabeled examples in the dataset.
2. Object Detection and Object Recognition
Object detection is the process of finding objects in an image. This includes determining whether there are any objects or not, what they are, where they are located, and how many there are. Object recognition is identifying specific types of objects based on their appearance. For example, if we were looking at a picture containing elephants and giraffes (among other creatures), our goal would be to identify which ones were elephants and which were giraffes. These two tasks—object detection and object recognition—are often used together for greater accuracy; however, they can also be done independently. Object detection aims to ensure that everything in an image has been identified correctly (i.e., each dog has been labeled as a dog). The goal of object recognition is only partially concerned with labeling everything correctly; instead, it focuses on identifying specific types of things within an image (i.e., all dogs but not cats).
3. Image Segmentation
Segmenting an image involves dividing it into smaller, more manageable pieces. It is widely used in computer vision and image processing applications. Image segmentation can be used to identify objects in images and separate them from the background.
Image segmentation is further divided into three classes:
Semantic segmentation: Semantic segmentation represents the limits between conceptually equivalent things. This technique is employed if exact knowledge of an object’s presence, position, size, or form inside a picture is required.
Instance segmentation: The objects in a picture are characterized by their existence, position, quantity, and size or form, all of which can be determined through instance segmentation. Thus, instance segmentation facilitates the identification of every object in an image.
Panoptic segmentation: Semantic and instance segmentation are combined in panoptic segmentation. For this reason, panoptic segmentation gives both semantic (background) and instance (object) labeled data.
4. Boundary Recognition
Boundary recognition is a type of image annotation, which means it’s used to describe the boundaries or edges in an image. It’s also called edge detection. Boundary recognition uses a mathematical algorithm to detect where edges are located in an image and then draw lines around them. This can help you segment images and identify objects within them.
Boundary recognition is used in many different applications, including object detection and object recognition, image classification, or just for your personal use as part of your workflow for annotating images with tags like “tagging faces” or “detecting buildings”.
Image annotation is the process of assigning attributes to a pixel or a region in an image. Image annotation can be done automatically, semi-automatically, or manually by humans. The annotation type depends on the use case, and it’s essential to understand what kind of data you’re trying to collect before choosing one technique over another. There are plenty of tools out there for doing this, ranging from simple online web apps to enterprise software solutions that integrate directly with your workflow management system (WMS).