Top 5 Myths About Outsourcing Image Annotation Services

Top 5 Myths About Outsourcing Image Annotation

More and more companies are choosing to outsource the annotation of their images as they realize the complexity of doing in-house image annotation. However, many companies that could benefit from outsourcing still choose not to outsource. This could be due to some persistent myths about the nature of outsourcing image annotation. It is often thought that choosing to outsource images isn’t compatible with innovation, image annotation tasks are too complicated to be outsourced, or outsourcing image annotation will compromise the security of data or intellectual property rights. Let’s take a close look at each one of these myths and see why they are only myths.

Myth 1: Outsourcing Image Annotation Is Incompatible With Innovation

Many companies believe that outsourcing image annotation will lead to annotated images only useful for the most basic tasks. In other words, companies looking to innovate in their respective fields and looking to use annotated data in new, insightful ways fear that outsourcing image annotation will lead to annotations that don’t meet their needs. While some image annotation companies may be content with simply applying a few bounding boxes to an image, professional annotation companies will work with their clients to understand their needs and tailor their datasets to the task the company is trying to accomplish. Quality image annotation services enable innovation, not stymy it. 

Myth 2: Data Annotation Compromises Data Security Or Intellectual Property Rights

One of the biggest reasons that companies needing image annotation shy away from outsourcing is that they fear losing control over their data. Keeping research secure is a valid concern, and image annotation companies understand this. Image annotation services appreciate the value of security and privacy for their client’s data, taking measures to make sure that the client’s data is handled fairly and stored securely. For instance, all image data should be stored in secured buckets and made accessible only to those who need to work on the images, and only for the time they need to complete the task.

Trustworthy image annotation companies will also apprise their clients of how their data will be stored, accessed, and managed. They will also come to an agreement with their client covering all potential intellectual property rights issues, like issues that may occur in the event of a merger or acquisition. The final solution is one that will benefit both customers and service providers.

Myth 3: Outsourcing Isn’t Needed, In-House Annotators Can Do The Job

The task of annotating an image for computer vision tasks often appears simple, resembling the simple addition of boxes around an object or the application of a colored filter to objects in an image. Yet image annotation is much more complicated than it often appears at first glance.

There are many different variables that must be considered when conducting image annotation, and image annotators are trained to take these variables into account. Let’s look at some examples of the variables that an annotator must consider:

There are many different variables that must be considered when conducting image annotation, and image annotators are trained to take these variables into account. Let’s look at some examples of the variables that an annotator must consider:

There are many different variables that must be considered when conducting image annotation, and image annotators are trained to take these variables into account. Let’s look at some examples of the variables that an annotator must consider:

There are many different variables that must be considered when conducting image annotation, and image annotators are trained to take these variables into account. Let’s look at some examples of the variables that an annotator must consider:

Appropriate Annotation Type

Whenever a database of images must be annotated, the annotators must be sure that they are using the appropriate type of annotation. For example, if high-precision is needed for the image classifier, semantic segmentation or instance segmentation should be used over bounding boxes. Using bounding boxes when pixel-level accuracy is needed will cause the classifier to be inaccurate, as too much of the background will be taken into account by the classifier. Similarly, if point tracking is necessary, semantic segmentation should not be used. It’s critical to have an understanding of the annotation task and the type of annotation required.

Annotation Quality

Beyond choosing the correct type of annotation, the annotations themselves must be high quality. Bounding boxes must be neither too loose or too tight. A bounding box is considered too loose when the interior of the bounding box contains unnecessary pixels, like the surrounding parts of the image. A bounding box is too tight when parts of the target object are found outside of the borders of the bounding box. Semantic segmentation can be poor quality if pixels of one object type are labeled as belonging to another object type.

Edge Cases

Edge cases are instances similar to the target case of annotation but differing in notable ways. One example of an edge case is an object which is notably obscured. If you are annotating images of animals, and one animal is halfway behind a tree, this would be an edge case. Other examples of edge cases are target objects that have been subjected to substantial blurring. It’s important to know how to account for edge cases, to understand when these cases should be included in the dataset and when they shouldn’t be.

These are just a few of the variables a professional annotator must account for. A professional annotator must also be able to account for these issues and carry out the annotation without sacrificing speed, as there are potentially thousands of images that require annotation in the dataset. For these reasons, in-house annotators will struggle to do the job of a professional annotator.

Myth 4: Third-Party Annotation Services Are Expensive

A constant concern of companies is that image annotation services will be too expensive to justify using them. However, it’s critical that the time and money spent developing deep learning systems aren’t undermined by poor quality data. There are two components to every image recognition system - data and algorithms. It does no good to invest heavily in the creation of sophisticated machine learning algorithms yet fail to create a quality dataset. 

Consider that what will genuinely waste time, money, and other resources are having poor quality annotations as a result of untrained in-house annotators. Though initial investment in professional annotation may seem expensive, it often saves resources in the long run, preventing poor-quality annotations that must be corrected.

Furthermore, some annotation companies even offer free samples of their work, so that you can determine if the annotation company will be a good match without investing any money.

Myth 5: Image Annotation Companies Can Only Handle Simple Tasks

Something that prevents many companies from outsourcing their image dataset to an annotation specialist is a fear that their machine learning task is too complicated, and that the image annotators will not be able to handle the complex requirements of their project. Companies often worry that only they fully understand their annotation needs and that image annotation companies are only capable of handling small, generic annotation projects. 

In reality, professional image annotators have achieved their experience through many hours of working on complex projects, coming to understand the best ways to annotate many difficult and expansive datasets. Image annotators may often understand the requirements of the annotation process better than their clients, due to their experience with a wide variety of annotation projects. If a client does require specific annotations, the annotation team will know how to take their needs into consideration and deliver a quality product.