ROLE OF SEMANTIC SEGMENTATION IN IMAGE ANNOTATION
With numerous techniques to train the algorithms, labeling data for computer vision can be quite challenging. Image annotation is one such method where one annotates the images containing the data of interest to make them understandable to the machines.
Within image annotation itself, there are so many techniques, one such being Semantic Segmentation. It is a complete labor-intensive technique that involves pixel-level accuracy to provide the right picture to computer vision.
To maintain accuracy, it is crucial to use the right tool for this technique. Let’s have a look at how to label data for semantic segmentation with the help of the right tool and human skills.
WHY SEMANTIC SEGMENTATION SHOULD BE USED FOR IMAGE ANNOTATION?
A general classification of the objects involves the following three process:
The process of image classification helps in recognizing the objects and existing data in an image, while the process of object detection allows us to find the accurate position of the object. With image segmentation, one can easily recognize and understand what exactly is in the image at the pixel level view. Each pixel in an image belongs to a single class in contrast to the object detection process where the bounding boxes of objects can overlap over each other.
As mentioned earlier, accuracy is one of the main factors for using semantic segmentation to build a computer-vision based application. Some of the top AI-based models used to get accurate vision are:-
–Medical Imaging Analysis
TYPES OF SEMANTIC SEGMENTATION
The two types of semantic segmentation that are the most popularly used at the image annotation services in Chennai are:
1) Region-Based Semantic Segmentation
Region-based semantic segmentation is mainly used in the type of segmentation that involves semantic-based classification and region extraction. A free-form region is usually selected by the model which transforms these regions into predictions at a pixel level in such a way that each pixel is visible to the computer vision. These are done by a specific type of framework.
While region-based semantic segmentation provides improved classification and performance, they also have few drawbacks such as they cannot produce precise boundaries.
2) Fully Convolutional Network-Based Semantic Segmentation
A Fully Convolutional Network-based semantic segmentation is created through a map that converts the pixels to pixels. However, these cannot generate regional classification as these are different. Fully conventional neural networks are used to create labels for inputs that have defined sizes as they have fully connected layers, being fixed in their inputs.
Fully Convolutional Network-based semantic segmentation can understand images that are randomly sized. While they work by running the inputs through various procedures, the final result has comparatively ambiguous object boundaries with low resolutions.
Not only these two, but there are also other widely used semantic segmentation models as well such as weakly supervised semantic segmentation, etc., that can create a large number of images with each segment in a pixel-wise manner. Manual annotation generally is very time consuming and costs a lot therefore some methods have been discovered recently to achieve drastic results in comparatively less time.
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