Image Annotation: A Quick Guide

What is image annotation?

Image annotation is defined as the task of labeling and image with human-powered work and, in some cases, computer-assisted assistance. Labels are predetermined by a machine learning engineer and are chosen to provide information to the computer vision model.

Image annotation frequently requires manual work. A Machine Learning engineer determines the labels, referred to as “classes,” and feeds the image-specific information to the computer vision model. After training and deployment, the model will anticipate and detect those preset features in new photos that have not previously been annotated.

What are the techniques for image annotation?

The five main techniques of image annotation are:

1. Bounding box
2. Landmarking
3. Masking
4. Polygon
5. Polyline

Bounding box

A rectangle box is drawn around the object to be identified. Bounding boxes can be used for both two- and three-dimensional images.

Landmarking

Landmark annotation labels objects within an image by placing points across the image. This type of labeling can be as simple as a single point to annotate small objects or as complex as multiple points to outline specific details. Maps, faces, bodies, and objects can all be used to annotate landmarks and so on with specific numbers by using this information ML model learns the parts of the human face.

Masking

Image masking annotation is used to shade a portion of the image classify object. Layer masking techniques can also be used to accomplish this. By using opacity we can make changes in the layers of the images. This technique can simply be used to hide unwanted content in an image, highlight key points, and improve image quality.

Polyline

The polyline technique aids in the development of machine learning models for computer vision, which are used to guide autonomous vehicles. It ensures that ML models recognize road objects, directions, turns, and oncoming traffic in order to perceive the environment for safe driving.

Polygon

Label irregularly shaped features with precise polygons drawn around any items of interest. Image polygon annotation is commonly used in automated object detection. Polygon annotation is a precise method of annotating objects by selecting a series of x and y coordinates along their edges. Polygon annotation can thus have pixel-perfect precision while remaining highly flexible and adaptable to a wide range of shapes.

The most common Image Annotation Formats

There are no specific formats for image annotation, which cannot be denied. However, the following are the most common:

COCO: This format is subdivided into keypoint detection, panoptic segmentation, image captioning, stuff segmentation, and object detection.

YOLO: Each image in the same directory receives a.txt file with the same name in this format. This.txt file contains annotations for the related picture file (height, width, object class, and other information).

Pascal VOC: It stores annotations in XML file format and provides standardized image data sets that aid in object class identification.

The Challenges of Image Annotation for Machine Learning

Human annotation vs Automated annotation:

The cost of data annotation varies according to the method used. Annotation using automated methods promising a given level of accuracy can be rapid and less expensive, but it risks annotation precision because the degree of correctness remains unclear until studied. Human annotation, on the other hand, takes time and is more expensive, but it is more accurate.

Consistently producing high-quality data:

High-quality training data produces the best results for any ML model, which is a challenge in and of itself. An ML model can only make accurate predictions if the data is of high quality and consistent. Subjective data, for example, is difficult to interpret for data labelers from various geographical locations of the world due to differences in culture, beliefs, and even prejudices – and this might result in diverse answers to repeated tasks.

Choosing the right Annotation Tool:

Producing high-quality training datasets requires the use of the appropriate data annotation tools as well as a well-trained workforce. For data labeling, several types of data are used, and knowing what factors to consider while selecting the correct annotation tool is critical.

Best Practices for Image Annotation for Machine Learning

Only high-quality datasets produce remarkable model performance, as we already know. The high performance of a model can be attributed to the precise and meticulous data labeling method discussed earlier in this text. It’s crucial to note, however, that data labelers use a few “tactics” to aid sharpen the data labeling process and producing excellent results. It’s worth noting that each dataset necessitates distinct labeling instructions for its labels. Consider a dataset to be an evolving phenomenon as you go through these procedures.

Use Tight Bounding Boxes:

The idea of employing tight boxes around items of interest is to assist the model to understand which pixels are meaningful and which are not. However, data labelers must be careful not to keep the boxes so close together that they cut off a section of the object. Simply make sure the boxes are small enough to hold the entire thing.

Tag or Label Occluded Objects:

What are occluded objects, and what do they do? Occlusion occurs when an object is partially blocked and kept out of view in an image. If this is the case, make sure the occluded object is labeled fully as if it were visible. In such instances, creating bounding boxes on the partially visible section of the object is a common mistake. It’s worth noting that the boxes may overlap if there are multiple things of interest that appear obscured (which is fine); nevertheless, as long as all objects are properly named, this shouldn’t be an issue.

Maintain Consistency Across Images:

The truth is that almost all items of interest have some degree of sensitivity when it comes to identification, which necessitates a high level of consistency during the annotation process. For example, in order to call a vehicle body part a “crack,” the extent of the damage must be consistent across all images.

Tag All Objects of Interest in Each Image:

Have you ever heard of false negatives in machine learning models? Computer vision models, you see, are designed to learn which patterns of pixels in an image correspond to an object of interest. In this regard, every appearance of an object should be labeled in all images to assist the model in accurately identifying the object.

Labeling Instructions Must Be Clearly Visible:

When labeling instructions are not set in stone, they should be explicit and shared in order to allow for future model modifications. To produce and maintain high-quality datasets, your fellow data labelers will rely on a set of unambiguous instructions piled safely somewhere.

Label Objects of Interests in Their Entirety:

When labeling images, one of the most fundamental and important best practices is to ensure that the bounding boxes encompass the entire object of interest. If only a portion of an object is labeled, a computer vision model may become perplexed as to what a full object consists of. Furthermore, ensure completeness; that is, label all objects in all categories in an image. Failure to annotate any item in an image impedes the learning of the ML model.

Final Thoughts

Since image annotation is very important for the overall success of your AI projects, you should carefully choose your service provider. TagX offers data annotation services for machine learning. Having a diverse pool of accredited professionals, access to the most advanced tools, cutting-edge technologies, and proven operational techniques, we constantly strive to improve the quality of our client’s AI algorithm predictions.  

With the perfect blend of experience and skills, our outsourced data annotation services consistently deliver structured, highest-quality, and large volumes of data streams within the desired time and budget. As one of the leading providers of data labeling services, we have worked with clients across different industry verticals such as Satellite Imagery, Insurance, Logistics, Retail, and more.