Cogito is providing the image semantic segmentation annotation service to detect, classify and segment the different types of objects in the image for machine learning algorithm training.
Object classification and segmentation – 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.
How they are Different from Each Other?
Though, first one is a kind of more precise classification of objects in an image of a single class, while later one simply classify the two different objects in a single image. Image annotation techniques are used classify such objects while in semantic segmentation the objects are detected, classified and segmented for computer vision.
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 analyze the numerical properties of various image features and organizes the data into the 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. At the first stage characteristic properties of image features are isolated and on the basis of this, a unique description of each classification category is created.
And at subsequent testing stage, these feature space partitions are used to classify the images features to differentiate from each other. And in machine learning, image classification is used for both – supervised learning and unsupervised learning.
Actually, Supervised and unsupervised classification is pixel-based classification process that creates square pixels and each pixel has a class. But object-based image classification groups pixels into representative shapes and sizes.
What is Segmentation in Image Processing?
Segmentation in an image is the process of the breaking down the digital image into multiple segments (that is divided into the set of different pixels into an image). The purpose of segmentation is to simplify or change the representation of an image into an easier format making to more meaning for machines to analyze.
Image segmentation is the process of assigning a label to every pixel in an image in such 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.
In semantic segmentation is basically used for 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 accuracy computer vision view to the machines.
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 - Semantic segmentation and instance based Segmentation is used for objects nested classification create objects having separate regions.
The difference between segmentation and classification is clear at some extend. And there is a one difference between both of them. The classification process is easier than segmentation, in classification all objects in a single image is 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.
Medical Image Segmentation for Deep Learning in Healthcare
Image semantic segmentation is also widely used for medical imaging analysis in healthcare sector. It is used in diagnosing the various types of diseases for deep learning to find out the accurate illness through computer vision and predict the possible outcomes helping doctors to take faster decision for right treatment.
Cogito is providing the image annotation service to detect, classify and segment the different types of objects in the image for machine learning algorithm training. It is also offering image semantic segmentation service for medical imaging analysis and self-driving cars to provide the best level of accuracy for computer vision.