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5 Crowdsourcing Image Annotation Benefits in 2024

The global market for computer vision (CV) or image recognition-enabled technologies is rapidly growing. To build and implement such solutions, businesses need a lot of visual data, which needs to be collected and annotated. Annotating such large volumes of data can be expensive and time-consuming, and while some companies have the resources to buy expensive automated tools and hire a dedicated team, others do not.

If your business is one of the latter ones and does not wish to spend a tidy sum on expensive annotation tools or hire a team, this article is for you.

Crowdsourcing image annotation can help business leaders streamline the annotation phase of their projects. In this article, we help explore:

  • How can image annotation tasks be crowdsourced? 
  • What are the benefits of crowdsourcing image annotation?
  • Why and when working with a crowdsourcing service provider can be beneficial?
  • Some best practices to consider while working with a crowdsourcing service provider

How can image annotation be crowdsourced?

Crowdsourcing image annotation is an effective way to annotate and label large volumes of image data with speed and accuracy. By leveraging the power of the crowd, businesses can complete their image labeling and annotating tasks for a variety of use cases, including machine learning, computer vision, and image recognition applications.

Through crowdsourcing, a company can post image annotation tasks in the form of micro-tasks or jobs for the public to complete them. In return, the company provides the public with some sort of compensation, typically monetary. 

A simple illustration explaining how crowdsourcing works

Top 5 benefits of crowdsourcing image annotation

1. Cost-effective

One of the major benefits of crowdsourcing image annotation is that it can be a cost-effective solution. It can help reduce the cost of manual annotation, which can be expensive when done in-house since it requires the development and maintenance of the platform. In crowdsourcing, a large pool of remote workers can annotate images at a fraction of the cost of a full-time in-house employee. For instance, if a startup wants to build an image recognition system for identifying dog breeds, it can save money and time in the long run by crowdsourcing image annotation tasks.

In another example, a study1 on image annotation for nucleus detection and segmentation in computational pathology, used crowdsourcing to save a significant amount of money and time.

2. Speed

High-quality data annotation is a time-consuming2 process; crowdsourcing can help speed things up. Since a large pool of workers is doing the image annotation tasks simultaneously, it takes significantly less time than doing the process in-house. As a real-world example3, we can consider the work of the nonprofit Global Fishing Watch. The organization tracks illegal fishing activities by using satellite imagery. Through leveraging crowdsourcing for image annotation, they annotated millions of images in a significantly shorter period of time.

3. Scalability

Crowdsourcing also offers a greater degree of scalability as compared to other image annotation methods. The large pool of remote workers allows businesses to scale up or down based on their needs change. A real-world application of this is how Google used crowdsourcing in a scalable way for its Quick Draw dataset4. The dataset contains over 50 million images of drawings that were crowdsourced to people worldwide to analyze and annotate.

An illustration of some doodles from the dataset and the logo of Google's Quick Draw tool

4. Diversity

Crowdsourcing can help bring diversity of perspective to image annotation tasks. It is one of the easiest ways of accessing talent from different countries, cultures, ethnicities, ages, groups, occupations, etc. This helps ensure the annotations are accurate and representative of a wider range of viewpoints. 

For instance, if a social media platform wants to improve its facial recognition algorithm, it will have to gather images of people from different age groups, ethnicities, skin colors, disabilities, etc. annotating such a diverse image dataset can be challenging if done in-house. Crowdsourcing can make this process easier.

5. Quality control

Human annotators can bring a high level of quality to the table. No automated tool can match it. The issue occurs when a human annotator is given 1000 images to label in a week. Then the process becomes repetitive, and the annotation quality falls. Crowdsourcing image annotation tasks can help overcome this issue. If you need to annotate 50,000 images in a week, only 500 crowdsourced annotators can do the job for you while maintaining the level of quality.

Top 5 benefits of using a crowdsourcing service provider for image annotation

There are several benefits to working with a crowdsourcing service provider for image annotation.

1. Less work

The main benefit of working with a crowdsourcing service is that you don’t need to worry about the logistics of the process. The service provider will already have a platform in place and will do the job for you for a much lesser cost than crowdsourcing yourself.

2. Larger and better crowd

A service provider will also be able to provide a much larger crowd than you can find yourself. This is mainly because they have spent years growing their crowd and making sure that the right people are hired.

3. Transfer of responsibility

Images are considered biometrics data, so crowdsourcing an image annotation job will have some ethical and legal considerations attached to them. Working with a crowdsourcing platform transfers that responsibility to the service provider and frees you from ethical and legal complications.

4. Better quality

Crowdsourcing service providers also have quality assurance practices and standards in place simply because they have been doing this job longer than you. You just need to clarify your image annotation quality standards, and the service provider will make sure to maintain them.

5. More secure

Finally, crowdsourcing service providers can also offer better data security. The service providers can ensure that the annotators sign non-disclosure agreements and follow strict security protocols to protect the data.

If you need help finding a crowdsourcing partner, check out this guide to compare 13 crowdsourcing platforms/service providers and help you select the right one for your business.

If you wish to outsource instead of crowdsourcing, you can check our data-driven list of data annotation/labeling/tagging/classification services to find the option that best suits your project.

Further reading

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References

  1. Irshad, H. et al. (2014). “Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd.In Pacific symposium on biocomputing Co-chairs (pp. 294-305). (Accessed: Feb 16, 2023).
  2. Mckinsey (March 25, 2021) “Five insights about harnessing data and AI from leaders at the frontier”. McKinsey Global Institute. (Accessed: Feb 16, 2023).
  3. Oceana, “Global Finishing Watch Report”. P. 4.. (Accessed: Feb 16, 2023).
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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