Why Image Segmentation is Needed: Image Segmentation Techniques

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.

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Image Segmentation Techniques

Basically, there are two broader categories of segmentation techniques — Edge-Based & Region-based, but various other image segmentation techniques are required to develop various AI models.

Threshold Method
Edge Based Segmentation
Region Based Segmentation
Clustering Based Segmentation
Watershed Based Method
Partial Differential Equation Based Segmentation Method
Artificial Neural Network Based Segmentation

All these types of image segmentation techniques are used for object recognition and detection in various types of AI model applications. In satellite imagery, image segmentation can be used to detect roads, bridges while in medical imaging analysis, it can be used to detect cancer. In image annotation, semantic segmentation is used to make the objects in the single class recognizable to machines.

The need for image segmentation is very much important especially in the AI world. Image segmentation applications are becoming more important due to demand in AI industry that is dedicatedly involved in developing the machine and deep leering models for different fields.

Anolytics is one of the top companies providing the data annotation service with expertise in image annotation to create the high-quality training data for machine learning models developed through computer vision algorithms. It is also offering the semantic image segmentation with high-level of precision for varied fields healthcare, retail, automotive, agriculture and autonomous vehicles.