An Image Labeling Guide: Image Annotation

You may have never heard of the concept of image annotation before. But what about the futuristic self-driving cars that have gone viral over the past few years? Well, image annotation is the thing that has made this game-changing technology possible. 

Image labeling or image annotation is necessary to train computer vision models, which are algorithms designed to make a computer visually interpret the object it identifies in the world. This cutting-edge technology has found widespread application in autonomous drones and vehicles, facial recognition, medical diagnosis, and other high-tech and AI-powered fields. To achieve the AI ambition to outperform human sight, the machine learning model must consume humongous quantities of vision images data. 

This article answers all the basic questions you may have about image annotation, Stick around till the end of the post for the most readily understandable introduction to photo annotation.

What is Image Annotation?

Image annotation refers to labeling images on digital devices through human input and sometimes with computer assistance. Image labeling is generally designed to train algorithms to complete tasks such as object detection, image segmentation, and image classification. Its practices include everything from annotation for one single photo to numerous labels within the same image

One good example of picture annotations is having human annotators label each image with the correct object name by outlining the boundaries and attaching the text tags specifying its feature. The annotated image datasets can be used to train computer vision models that use human annotations and learn to identify these labeled objects in unannotated pictures automatically. 

Whether the computer vision model can surpass human sight’s capabilities highly depends on the overall precision and accuracy of the machine learning images datasets provided for its training. As a result, it’s of paramount importance to collaborate with a reliable and quality-driven image annotation company when sourcing raw images for computer vision AI model training. 

Different Types of Image Annotation

There are four major types of image annotation, with each differs from another in revealing particular features or areas within the picture. AI developers can decide on the best-fit image annotation type based on the desired datasets for training the algorithms.

Image Classification

In image classification, the machine learning model is trained to recognize the presence of an object in an unannotated image resembling something already labeled in the previously annotated pictures drawn from the entire training dataset. 

An image can be classified at a high level depending on a particular intended feature. For instance, the labeling images of a house interior can be tagged as “dining table” “sofa”, “living room”, or “kitchen”. Or, a human annotator could annotate outdoor images with labels like “day” or “night”.

Object Detection

Object detection/recognition involves accurately identifying one or more target objects and determining their positions and number in an unannotated image. The computer vision images model can be trained to automatically identify a similar object in unannotated images by repeating the object-detecting process with different raw pictures. You can use object recognition techniques such as polygons or bounding boxes to separately annotate different objects in the same image. 

Semantic Segmentation

Semantic segmentation is the next step after a particular object is classified and detected through a bounding box, which focuses on the surroundings of the target object in detail. It delineates boundaries between target similar objects and annotates them with the same identification labels. This type of image annotation is used to understand the presence, position, size, and shape of particular objects. In semantic segmentation, the object labeling is pixel-wise, which means every pixel in the image is annotated. 

Instance Segmentation

Instance segmentation, also referred to as object class, combines the features of object detection and semantic segmentation by tracking presence, position, size, and shape and counting the number of one or multiple objects of the same class in an image. In instance segmentation, every instance of an object is annotated with a unique tag, enabling easier identification after.

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How to Annotate an Image?

How to attach annotations to your raw image data? You can leverage image annotation tools to perform image labeling tasks. Annotation tools for picture annotations are available in commercial markets or via open source and freeware. If you have the knowledge and toolkit to customize and maintain the open-source tool, you can figure out how to label images and do the job yourself. If not, you can also use an open-source tool provider or work with a professional image annotation company.  

The Advantages of an Image Annotation Company

Image annotation contains enormous work in collecting, cleaning, and annotating images. It combines the use of image annotation tools, processes, and people. Using an image annotation service can streamline the project process and improve and guarantee the quality of final image annotation results. The performance of your machine learning images model goes hand in hand with the quality of the annotated image datasets. A professional image annotation company usually has access to quantities of dedicated AI data annotators with domain knowledge and even the required image annotation experience relevant to the task. These annotation companies also provide professionally-trained in-house, contracted, or managed teams to scale your image annotation requirements and processes. 

Summarizing Image Annotation

Image annotation, an indispensible technology to train machine learning algorithms for object identification, is crucially important to the development and shaping of an AI-powered future. There are various techniques for picture annotations. Which one to use for AI data training is up to what the developers want the computer vision image models to consider. When it comes to sourcing effective image annotation at scale, employing a professional image annotation service is key.

Wordspath can help

Wordspath provides many cost-effective linguistic and AI data training solutions such as image annotation services relevant to diverse subject matters with accuracy and at scale. 

We take pride in our quality-driven workflow that combines the excellent work of our linguists, desktop publishers, project managers, customer service, and technical team. Their endless support allows Wordspath to provide first-rate language solutions in 150+ languages for thousands of customers who need to connect with the world.

Wordspath also offers machine translation post-editing services translating the content with our proprietary MT engine and having our in-house/contracted linguists review,  edit, polish, and proofread the results.

Meanwhile, we are highly experienced in delivering tailor-made localization-related solutions such as desktop publishing, transcription, subtitling, and voiceover. Our ability to quickly handle a wide range of content types between nearly all language combinations sets us apart from our competitors. Should you need to consult on your best-fit language solution, you can contact us through live chat or email to Or simply request a free quote.

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