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What Is Image Annotation, Semantic Segmentation And Automatic Annotation: With Difference


All these are the part of image annotation but there is difference between all of them as one of them is done for creating training data for machine learning and technique of second one.

Finally the last one is like second one but done with the help of AI-enabled machines while second one is done by humans manually using tools. So, to differentiate both of them we will start from second one and discusses about other relevantly.

What is Image Annotation?

It is a process of labeling the data (available in the format of images), to make the object in the image recognizable to machines through computer vision technology. Basically, its used to detect, classify and group the objects in machine learning training.

And there are different types of image annotation techniques used to annotate the object of interest in an image with accuracy. It is overall process of data labeling done on images done by humans manually, or automatically using AI-enabled software.

What is Image Semantic Segmentation?

Semantic segmentation is the type of image annotation technique used to detect, classify, localize and segment the object for computer vision. It is used for more precise recognition of objects in a single class to differentiate them from each other.

Image semantic segmentation is mainly used to build a computer-vision based application that requires high accuracy. AI-based models like face recognition, autonomous vehicles, retail applications and medical imaging analysis are the top use cases where image segmentation is used to get the accurate vision.


What is Automatic Image Annotation?

Automatic image annotation is the process of assigning the metadata in the form of keywords, captioning and annotation done through software or computer tools.

The machine learning based AI tools annotate the images using the automated software that can recognize the objects in the images and annotate the same precisely.

Basically, in automatic annotation, one or more semantic tags are done to reflect the content of a specific image through ML algorithms. Image annotation is based on image feature representations and features utilized in different tasks with the ability to represent differently with best level of accuracy.

Types of Image Annotation used for Computer Vision in Machine Learning

See here Types of Image Annotation used for Computer Vision in Machine Learning, AI and other fields given below-
  • Bounding Box Annotation
  • 2D and 3D Cuboid Annotation
  • Landmark Annotation
  • Polyline Annotation
  • Text Annotation
  • Polygon Annotation
  • Semantic Segmentation Annotation
  • Video Annotation
  • Live Annotation
  • ADAS Annotation
  • NLP Annotation
  • Live Annotation
  • Medical Image Annotation

Difference between Image, Semantic Segmentation and Automatic Annotation

I think you got know what are the actual definitions of annotations and how they are different from each other. And already told you the difference in starting para, but let revise again and make it clear how exactly they are different from each other.

Image annotation is the process of making an object in an image recognizable to machines through computer vision. And this process can be done by humans manually, using the tools and software, while automatic image annotation is done by the automatic software that are developed through AI or machine learning models.

And lastly image semantic segmentation is the part of image annotation, either it is done manually or automatically. You can say it is a type or technique of image annotation to make the objects more clearly recognizable in a single class while using the supervised machine learning process to develop a right AI model for right prediction.

Cogito is one of the companies providing the the image annotation service with image semantic segmentation for machine learning and AI. It is also providing the data annotation for wide-ranging industries including healthcare, retail, agriculture, automotive, robotics, autonomous machines and other fields.

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