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Segmentation VS Classification in Image Processing?

Segmentation VS Classification


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Segmentation VS Classification in Image Processing

Object Segmentation VS Classification – both are part of machine learning-based image processing to train the AI algorithms through computer vision. And both are important for object recognition precisely in machine learning and AI development. 

Segmentation VS Classification – How they are Different from Each Other? 

Although, the first one is a kind of more precise classification of objects in an image of a single class. While latter one simply classifies the two different objects in a single image. Image annotation techniques are used to classify such objects while in semantic segmentation the objects are detected, classified, and segmented for computer vision.  

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What is Image Classification and How it Works?    

Image classification actually, refers to the task of extracting the information classes from a multiband raster image. It analyzes the numerical properties of various image features and organizes the data into different categories. Or you can say image classification is like image categorization. 

In fact, data classification algorithms typically employ two phases of processing – training and testing. In the first stage, the characteristic properties of image features are isolated. And on the basis of this, a unique description of each classification category is created. 

At the subsequent testing stage, these feature space partitions are used to classify the images’ features to differentiate them from each other.  And in machine learning, image classification is used for both – supervised learning and unsupervised learning.  

Actually, Supervised and unsupervised classification is a pixel-based classification process that creates square pixels and each pixel has a class.

What is Segmentation in Image Processing?

Segmentation in an image is the process of breaking down the digital image into multiple segments. That is divided into the set of different pixels in an image. The purpose of segmentation is to simplify or change the representation of an image into an easier format. And it is making it more meaningful for machines to analyze.    

Image segmentation is the process of assigning a label to every pixel in an image in such a way that pixels with the label share certain characteristics. It is mainly used to locate objects and boundaries like lines and curves in the images.    

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Semantic segmentation is basically used for a more accurate view of an image. It can recognize and understand what exactly is in the image at pixel level view in a single class to provide an accurate computer vision view to the machines.  

Segmentation VS Classification

Semantic segmentation is useful in detecting and classifying the object in an image when there is more than one class in the image. Hence, there are two popular techniques are used. One is Semantic segmentation. Another is instance-based Segmentation is used for object nested classification to create objects having separate regions. 

The difference between segmentation and classification is clear to some extent. And there is one difference between both of them. The classification process is easier than segmentation.

In a classification, all objects in a single image are grouped or categorized into a single class. While in segmentation each object of a single class in an image is highlighted with different shades to make them recognizable to computer vision. 

Cogito is providing the image annotation service. Which will help to detect, classify and segment the different types of objects in the image. That would relate to machine learning algorithm training to get an expert. Also offers image semantic segmentation services for medical imaging analysis. As well as self-driving cars to provide the best level of accuracy for computer vision. 

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