Image Annotation

Defining Image Annotation

  1. Image annotation for AI projects
  2. Image annotation types
    1. Key points annotation
    2. Lane annotation
    3. Polygon annotation
    4. Skeletal annotation
    5. Bounding boxes
  3. Image annotation services and tools
    1. Image annotation services
    2. Annotation services vs automated tools
  4. Image annotation use cases
  5. A Beginner's Complete Guide to Image Annotation for Machine Learning

Image annotation for AI projects

Image annotation is an essential but challenging process for many AI companies. Creating effective, efficient image training datasets for machine learning can be a drain on resources and focus for innovators. Management and training concerns can be a distraction for company leaders when they should be focusing on core development goals.
Outsourcing image annotation ensures that computer vision projects have access to precise training images whilst maintaining flexibility and oversight.

Image annotation types

We use a number of different techniques when applying information to AI training images. We can create training data that reflects the diversity of the real world by using these different options:

BOUNDING BOX ANNOTATION

POLYGON ANNOTATION

SEMANTIC SEGMENTATION

SKELETAL ANNOTATION

KEY POINTS ANNOTATION

LANE ANNOTATION

INSTANCE SEGMENTATION

Bitmask ANNOTATION

CUSTOM ANNOTATION

BOUNDING BOX ANNOTATION

BOUNDING BOX ANNOTATION

This is the fastest annotation technique, and also the most common. Using annotation platforms workers drag bounding boxes around objects.

However, the downside of this technique is that it does not fully capture precise shapes.

POLYGON ANNOTATION

POLYGON ANNOTATION

This annotation type is essential if developers are looking to capture irregular or complex shapes.

Annotators connect small lines together at vertices, making their way around the shape of an object. Polygon annotation supports semantic segmentation methods because it enables the division of each pixel into classes.

SEMANTIC SEGMENTATION

SEMANTIC SEGMENTATION

Keymakr’s advanced annotation tools and our professional in-house annotation team ensure the best results for your computer vision training data needs.

Annotating videos while tracking objects through multiple frames. Each object on the video will be recognized and tracked even through different cameras or separate video segments.

SKELETAL ANNOTATION

SKELETAL ANNOTATION

This technique allows AI models to identify and interpret the bodies and movement of humans.

Annotators attach lines to limbs and join them together at body points, like shoulders or elbows. Additionally, developers use skeletal annotation to produce training data for sports and home fitness AI applications.

KEY POINTS ANNOTATION

KEY POINTS ANNOTATION

This technique is used to label important, single points in images.

Key point annotation can locate specific features in images of human faces, it can also pinpoint crucial parts of structures like buildings and bridges.

LANE ANNOTATION

LANE ANNOTATION

Linear and parallel shapes and structures are traced using this technique.

Annotators use a lining tool to track the shape of objects like roads, railway lines and pipelines.

INSTANCE SEGMENTATION

INSTANCE SEGMENTATION

Keymakr’s advanced data annotation tools and our professional in-house annotation team ensure the best results for your computer vision training data needs.

Annotating videos while tracking objects through multiple frames. Each object on the video will be recognized and tracked even through different cameras or separate video segments.

BITMASK ANNOTATION

Bitmask ANNOTATION

Keymakr’s advanced annotation tools and our professional in-house annotation team ensure the best results for your computer vision training data needs.

Annotating videos while tracking objects through multiple frames. Each object on the video will be recognized and tracked even through different cameras or separate video segments.

CUSTOM ANNOTATION

CUSTOM ANNOTATION

Keymakr’s advanced video annotation tools and our professional in-house annotation team ensure the best results for your computer vision training data needs.

Annotating videos while tracking objects through multiple frames. Each object on the video will be recognized and tracked even through different cameras or separate video segments.

Image annotation services and tools

There are lots of options for AI companies in search of image annotation. Companies can choose to use automated image labeling technology to produce datasets. Alternatively, they can choose to employ the services of data annotation providers, like Keymakr.

image annotation services

Image annotation services

Outsourcing data annotation to dedicated services, like Keymakr, saves computer vision developers time. It also guarantees high-quality and responsive image annotation. Human workers must locate, outline and label objects and people in order to annotate images. A wide variety of images require this careful annotation treatment.

Consequently Keymakr employs a large team of experienced annotators who construct exceptional datasets, according to the most demanding specifications.

Automated annotation

There are a number of AI assisted labeling tools that can speed up the image annotation process. This sometimes means producing automatic polygon outlines of objects for semantic segmentation, but it can also mean auto-labeling objects across thousands of images.

automated annotation
ANNOTATION SERVICES VS AUTOMATED TOOLS

Annotation services vs automated tools

Auto-labeling platforms can create training datasets quickly. By bypassing many human performed labeling tasks this technology reduces labour time and labour costs. However, relying too heavily on automated annotation can have a negative impact on image data quality.

Keymakr’s in-house team is capable of working with a wide range of annotation methods and types. They are also led by experienced managers who know how to guide a large scale image annotation project.

In conclusion, outsourcing to annotation providers lifts the burden of hiring and training from AI innovators, whilst maintaining a high level of precision and quality control.

Smart task distribution system

Keymakr’s worker analytics capabilities make it easy to assess the skills and performance levels of individual annotators. With the help of this information Keymakr’s smart task distribution system assigns tasks to annotators according to their strengths and weaknesses. This means higher levels of precision and productivity.

24\7 monitoring and alerts

Keymakr’s proprietary platform also allows managers to see information about the progress of a project at any time. The platform also gives alerts when there are any problems with data quality, or if a labeling task is off schedule.

Vector or bitmask

Keymakr offers both bitmasks and vector graphics to suit the needs of any computer vision project. Keymakr can also easily convert between image types if necessary.

smart task distribution system

Image annotation use cases

Keymakr provides AI companies with accurate and flexible image annotation. As a result Keymakr has played a role in a number of exciting AI use cases:

automotive industry

Automotive industry

Keymakr annotated over 20,000 images of roads from Europe and North America. Annotators then segmented these images, and assigned each pixel to a particular object class (cars, trees, sky, road, signs).

These training images allow autonomous car models to reliably identify objects in their surroundings and navigate accordingly.

Skeletal annotation for sports

Adding skeletal annotation to images of sports can help computer vision models to interpret human body positions. Skeletal annotation powers sports training and coaching AI applications.

skeletal annotation for sports
polygon annotation for the insurance sector

Polygon annotation for the insurance sector

By adding polygon annotation to images, annotators can help image data to capture complex and irregular shapes.

By outlining images of car parts with this technique, Keymakr’s team created a dataset for an insurance industry client.

The computer vision model trained with this data is capable of rapidly, accurately and autonomously processing insurance claims.

A Beginner's Complete Guide to Image Annotation for Machine Learning

Why does image annotation matter in the world of machine learning? The answer is simple. The images you use to train, validate, and test your algorithms will directly impact the performance of your AI project.

Every image in your datasets matters. The goal of a training dataset is to train your AI system to recognize and predict outcomes—the higher the quality of your annotations, the more accurate and precise your models are likely to be.

But image annotation isn’t always easy, especially if you’re dealing with large quantities of diverse data. Getting familiar with ML image labeling is one of the fastest ways to get to market with a high-performing, meaningful machine learning model.

Interested in boosting the performance of your next AI project? We’ve done the legwork and put together a comprehensive guide to image annotation types, tools, and techniques.

Image annotation


What is image annotation?

Image annotation is the process of labeling an image to show a machine learning model which features you want it to recognize. Annotating an image creates metadata through tagging, processing, or transcribing certain objects within the image.

Training a machine learning model to recognize desired features requires the principles of supervised learning. The goal is for your machine learning model to identify desired features in a real-world environment—and make a decision or take some action as a result.

Image Annotation Types and Techniques

There are many different types of image annotations. Each one is distinct in how it classifies particular features or areas of an image. Here are a few examples:

  • Image classification: This form of annotation trains your model to recognize the presence of similar objects based on similar collections of objects that it’s seen before. For example, a data annotator using image classification could tag a kitchen scene as “kitchen.”
  • Object detection: Otherwise known as object recognition, this type of image annotation detects the presence, location, and number of certain objects in an image. For example, a street scene can be separately annotated with bikes, pedestrians, vehicles, and other objects.
  • Segmentation: There are two main types of image segmentation. Semantic segmentation outlines the boundaries between similar objects (e.g., stadium vs. crowd) while instance segmentation labeling marks the occurrence of every individual object within an object class (e.g., every person in the crowd).

Image annotation types


In addition to tools, there are a variety of image annotation techniques. Commonly used methods include:

  • Bounding box labeling: Annotators draw a box around target objects.
  • Landmarking: Characteristics (such as facial features) within the image are “plotted.”
  • Polygon labeling: Irregular objects are annotated by their edges.

Image Annotation Types and Techniques

The right image annotation tool can help get the job done faster and with fewer errors using automatic image labeling. These are available on today’s market as open source or freeware image labeling tools.

If you’re working with an immense volume of data, you will need an experienced team of data annotators. Depending on the diversity of your datasets, more than one type of image annotation tool will be required.

Image annotation can often be a daunting task. Without the right tools, techniques, or workforce, you compromise on quality, precision, and the time it takes to get to market. That’s why AI companies often rely on professional data annotation services to label datasets for machine learning.


Keymakr Demo


Professional Image Annotation Services

Machine learning models are only as good as the data that is used to train them. Keymakr has the skills, equipment, and expertise necessary to deliver pixel-perfect results that align with your timeframe and budget.
Are you interested in high-quality training datasets that have been labeled according to your standards and specifications? Get in touch with a team member to book your personalized demo today.

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