In computer vision world, objects can be viewed through images. And classifying, tagging, segmenting and annotating these are images are important to make the objects of interest perceivable to machines. And in AI world, computer vision is playing big role helping the models understand the scenario around the world making AI possible through machine learning or deep learning.
What is Image Segmentation?
Image segmentation is the process of partitioning of digital images into various parts or regions (of pixels) reducing the complexities of understanding the images to machines. Image segmentation could also involve separating the foreground from the background or assembling of pixels based on various similarities in the color or shape.
Image Segmentation in Machine Learning
Various image segmentation algorithms are used to split and group a certain set of pixels together from the image. It is actually the task of assigning the labels to pixels and the pixels with the same label fall under a category where they have some or the other thing common in them.
And using these labels, you can specify boundaries, draw lines, and separate the most required objects in an image from the rest of the unimportant one.
In machine learning — image segmentation helps to make these identified labels further use for supervised and unsupervised training that is mainly required to develop machine learning based AI model. Image segmentation is used for image processing into various types of computer vision projects.
Why Image Segmentation is needed?
In image recognition system, segmentation is an important stage that helps to extract the object of interest from an image which is further used for processing like recognition and description. Image segmentation is the practice for classifying the image pixels.
And there are various image segmentation techniques are sued to segment the images depending on the types of images. Actually, compared to segmentation of color images is more complicated compare to monochrome images.