Data, in general, is the lifeblood of assisted Machine learning projects. The more data you have, the more accurate the end-product will be. However, it is not simply enough to have raw data. You need to have this data annotated so that the machine learning algorithm can properly identify the objects in a given image, understand human speech, and many other functionalities.

Even on the surface, we can see the correlation between correctly annotated data and the success of the project. However, this is also supported by research since according to some estimates, 80% of AI project development time is spent on preparing the data. The reason data annotation is so important is that even the slightest error could prove to be disastrous. There are different types of annotations performed as per project requirements. Image Segmentation is the most pixel-perfect type of annotation.

Image Segmentation

It is a pixel-level annotation task. The objects are segmented individually and classified as per the class labels. Since it is done at the pixel level the time consumption is more but at the same time gives deep predictions. Image segmentation is the task of partitioning an image into multiple segments. This makes it a whole lot easier to analyze the given image.

This is quite similar to grouping pixels together on the basis of specific characteristics. Now these characteristics can often lead to different types of image segmentation, which we can divide into the following:

• Semantic Segmentation
• Instance Segmentation

Let’s take a moment to understand these concepts.

Semantic Segmentation

Semantic segmentation treats multiple objects of the same class as a single entity. In semantic segmentation, all objects of the same type are marked using one class label. For example, if the image contains two persons, both of them will be marked with the same label for ex. Person.. It helps the visual perception model to learn with better accuracy for right predictions when used in real-life.

Semantic annotation tells you the presence and shape of objects, but not necessarily the size or shape. For example it can tell you that the image contains bananas but not the number of bananas present.

Instance Segmentation

It is a refined version of Semantic Segmentation. instance segmentation treats multiple objects of the same class as distinct individual objects (or instances). In instance segmentation similar objects get their own separate labels. For example, if the image contains two persons, both of them will be marked with the different labels like Person1 and person2. Typically, instance segmentation is harder than semantic segmentation.

Instance segmentation takes semantic segmentation to the next level by revealing the presence, shape, size, count, and location of the objects featured in an image. For example, it can tell you that the image contains bananas and the number of bananes present with their specific locations. Instance segmentation is used when information of every pixel is critical and may influence the accuracy of the perception model.

In other words, semantic segmentation treats multiple objects within a single category as one entity. Instance segmentation, on the other hand, identifies individual objects within these categories.

To achieve the highest degree of accuracy, computer vision teams must build a dataset for instance segmentation. Also it completely depends on your use case , that which type of segmentation will accurately train your model.Whether you’re using semantic or instance segmentation, you can perform pixel-wise segmentation, which includes every pixel within the outline of an object, or boundary segmentation, which only considers border coordinates.

Applications:

Sometimes bounding boxes simply aren’t accurate enough.There are several applications for which semantic segmentation is very useful.

1. Medical Images: Automated segmentation of body scans can help doctors to perform diagnostic tests.

2. Autonomous Systems: Autonomous vehicles such as self-driving cars and drones can benefit from automated segmentation. For example, self-driving cars can detect derivable regions.

3. Geographical Image Analysis: Aerial images can be used to segment different types of land. Automated land mapping can also be done.

Concluding Thoughts

The definitive goal of a computer vision project is to develop a deep learning algorithm capable of detecting objects in real-time with high-accuracy. For computers, machines need to learn from hundreds to thousands of labeled or annotated images. So it is important to pick a partner that understands the nuances of these image segmentation techniques. Be sure to do some research and identify those that can simply use computer vision versus those that can drill down to help build a robust data set of images that will take your projects to the next level.

TagX provides you with high-quality training data by integrating our human-assisted approach with machine-learning assistance. Our text, image, audio, and video annotations will give you the courage to scale your AI and ML models. Regardless of your data annotation criteria, our managed service team is ready to support you in both deploying and maintaining your AI and ML projects.