Image Annotation for Computer Vision & Machine Learning

Data labeling and annotation services have become significantly popular and important for several businesses, in several industries, and image annotation is a crucial part of both these services. Image Annotation in Machine Learning and Deep Learning refers to the process of labeling, categorizing, and classifying images, adding metadata to a dataset using text labels and annotation tools to represent the data features that the ML model has to identify on its own. ML professionals can use these annotated images to train the ML model with supervised learning. Once the model is deployed, the system should be able to identify unannotated images as well. Image annotation involves the use of a huge volume of data which Ml experts need to train, validate, and test so that the system generates the desired results. 

Image Annotation Techniques

Bounding Box

In the Bounding Box technique of image annotation, annotators draw boxes around the objects, for the objects that are symmetrical, or of less interest. Bounding boxes can be both 2D and 3D.  

Landmarking

Landmarking is an image annotation technique that marks and tracks the characteristics in a given image such as facial features, expressions, and emotions, along with labeling the position and alignment of a body by annotating pose points. 

Polygon

The image annotation technique is used to mark the vertices or the highest points of the target object in any given image and annotate the boundary, outline, and edges of the image. 

Masking

Masking is a technique that comprises pixel-level image annotation. This is done mainly to hide the unimportant parts of a given image while shifting the focus to the more important areas of focus. 

Polyline

The Polyline technique plots and tracks the continuous lines and line segments when working with open shapes like power lines or sidewalks. 

Transcription

Transcription is an image annotation technique that annotators use to annotate text in images and videos with multimodal data such as both image and text. 

Tracking

Tracking is another image annotation technique that helps annotators label and tracks the movement of any object in a given video across multiple frames in the video. Annotators also use image annotation tools that enable interpolation tracking the object’s movement even in the frames that are not annotated. 

Types of Image Annotation

Image Classification

Image Classification is the primary type of image annotation that is used to spot the presence of similar objects in a given image across a widespread dataset. Image Classification helps train machines to identify specific desired objects in unannotated images that are similar to any object in the labeled images the machine has been trained with. The images are prepared for classification by a simple process called tagging. 

Segmentation

Segmentation is an advanced version and type of image annotation used to analyze image content to determine whether the objects in it are the same or whether they differ from each other. Segmentation is further divided into three specific categories i.e., Semantic Segmentation, Instance Segmentation, and Panoptic Segmentation. Semantic segmentation helps label the boundaries of objects in a given image to help annotators identify the presence, location, size, and shape of the objects of interest. Instance segmentation or object class or pixel-wise segmentation is used to track the presence, location, count, size, and shape of all the objects in a given image. Panoptic segmentation is a combination of semantic segmentation

and instance segmentation to label a given image for both background and objects. 

Object Detection

Object Detection is another type of image annotation that helps identify the presence, and location of one or more than one objects in any given image to label them. This process is repeated by annotators with different sample images to train the ML model to identify unlabelled objects in new images automatically, on its own. Different objects in a single image can be annotated with the bounding box technique and also the polygon technique.

Boundary Recognition

Data sourcing companies are using Image Annotation to train a machine with labels used to identify lines and boundaries of objects in a given image. The boundaries refer to the edges or outlines of every individual object in the image, along with topography in the image and man-made boundaries. When the annotation is done right, the images will help ML algorithms identify objects and patterns in unannotated images as well. The main purpose of boundary annotation is to enable ML-powered systems to identify lines such as land outskirts, sidewalks, traffic lanes, and the like. The most popular use case of boundary recognition through image annotation is to ensure safe driving for autonomous vehicles. Boundary recognition also helps ML systems to differentiate the foreground from the background. 

Wrapping Up!

When working with computer vision models and data annotators, Image Annotation is integral to building robust ML and AI systems. Image annotation requires labeling of images and the labeled images are used to train the Machine Learning and Artificial Intelligence algorithms to generate the desired results. Image annotation has contributed to multiple industries that are working in the AI workspace. A few use cases of image annotation and machine learning would include self-driving cars, smart diagnosis, crop analysis, user behavior prediction, and more.