What is Image Annotation?

Image annotation is the process of annotating or labeling images in a given dataset. These data sets are used to train an ML model. By annotating images, you add labels to the features of the images that you expect your ML algorithm to recognize and get trained in recognizing these features in the future. Annotation is part of supervised learning.

Importance of Image Annotation in Machine Learning

Computer vision is a field of AI that enables computers to understand and interpret information from images, videos or other different types of inputs. In a sense, this enables a computer to have “vision” like a human being. 

Great computer vision gives us incredible AI applications such as unmanned vehicles, traffic controls, self-driving cars or medical applications such as cancer detection and more.  Image annotation has a major role in the development of computer vision. 

To train these computer vision machine learning algorithms to interpret visual information (particularly images,) we feed it with training data. In order to develop this training data, we annotate the images present in these data sets. Thus image annotation plays a primary role in the creation of these computer vision models. 

This training data consists of datasets containing hundreds of thousands or possibly more images. These images come with detailed labels of what they are and what they contain.

This detailed information, called as labels or annotations, help the model interpret what is contained in the images and develop an understanding of what comes next.

What Are The Different Types of Image Annotation?

Primarily, there are 4 different types of image annotations and these are the most often used types. They are image classification, object detection & recognition, semantic image segmentation, boundary recognition. 

1. Image Classification

This type of image annotation refers to the identification of similar objects present in an image across the dataset. This is used to train an ML algorithm to detect and recognize similar objects like it has been trained with the labeled training data. This form of image annotation is also called “tagging”.

Generally, classification is applied at a high level, across the entire image. For instance, annotators can tag the images of a car as steering, seat, windshield or images of mountains with labels like sky, cloud, land, rocks or water. These labels are very high level and could be just information regarding the images.

2. Object Detection & Recognition

Object detection and recognition is when you annotate these images in order to train algorithms to detect specific objects in a given image. Here, annotation is dependent on labeling all or specific objects in a given image from the data set. 

The ML algorithm is fed with a large data set of these annotated images and is trained to detect these specific objects in the images. After being trained with enough data, the ML algorithm will be able to identify the objects even in unlabeled images all by itself. 

This type of computer vision is very important and has great potential in the medical fields such as Computer Tomography (CT Scans) or MRI Scans. Since such data is multi-frame, you can annotate it continuously. Specific use cases include detecting indicators for cancer or other general use cases such as in traffic, detecting cars, trucks, pedestrians etc.

3. Image Segmentation

Segmentation is an advanced application of image annotation. The applications of segmentation vary from being used to analyze images to find out similarity or difference between images (OR) identify differences in an image over a period of time.

Segmentation is available in 3 different types. 

  1. Semantic Segmentation
    This is used to delineate and label the boundaries between similar objects. It is generally used to understand the location, size, shape or the presence of objects. Semantic segmentation is mostly used for grouping objects and when you cannot count or track multiple images. For instance, you could annotate the image of a basketball game court to segment the seated crowd.
  2. Instance Segmentation
    When you need to count and track the count, location, presence, shape or size of objects in an image. Also known as object classification, you can imagine the above example of the basketball game. Instance segmentation allows you to not only label all the individuals in the crowd but also how many of them are present in the crowd. 
  3. Panoptic segmentation
    Panoptic segmentation is a blend of both instance and semantic segmentation. This will give the labeled data both object (instance) and background (semantic). For example, you can use panoptic segmentation for satellite images. It can find changes in protected geographical areas. Scientists can track changes across planes or track the growth of certain flora or forest fire etc.,

4. Boundary Recognition

Image annotation is used to create training data to teach a machine to define the lines and boundaries of objects in a given image. These boundaries can mean anything ranging from the edge of an object to topographical areas as well. Man made boundaries or natural boundaries. 

Applications for this type of annotation are wide. They include creating training data for training ML algorithms to identify lines, traffic lanes, geographical boundaries, power lines, exclusion zones etc. Right boundary detection will improve the safe operation of automatic driving cars and other vehicles. 

For a high quality computer vision, you need high quality machine learning algorithms. For high quality machine learning algorithms, you need high quality training data.

Diffgram is the only truely open source data labeling platform that you need to improve the quality of your training data and thus, your ML efforts. It has every tool you’ll ever need to create high quality annotations for your images, video,

Get started in creating high quality image annotations with Diffgram today. Install it or try it online (no CC required, no commitments)