Labeling the objects in an image with classes or tags provides pixel-accurate training data for machine learning models—the backbone of computer vision. Image annotation is the tedious process of labeling the objects within the images to make objects recognizable to machines. For example, imagine that you wanted to identify cats in images. To build an AI-based model to single out cats, one needs to annotate thousands of cats based on the color, types, size, habitat, etc.
Though several image annotation techniques exist, semantic segmentation is a pixel-level annotation that provides a precise annotation scale for computer vision. Though it is an expensive approach for image annotation, it is the most preferred technique to maintain pixel accuracy.
Why does Semantic Segmentation matter for computer vision?
Computer vision sees objects differently, requiring a specific set of steps: classification, object detection, and image segmentation.
- Image classification is helpful to recognize similar things in an image.
- Object detection gets you one step closer to finding the exact position of the object of interest (bounding box annotation).
- Image segmentation allows you to analyze the properties within an image at the pixel level.
Semantic segmentation is the process by which you assign a class per pixel in an image. Pixel-by-pixel image segmentation offers a far more granular understanding of an image. The primary use of semantic image segmentation is to build a computer-vision-based application that delivers a high degree of accuracy. Some of the top use cases of image segmentation for AI-augmented models are face recognition, autonomous vehicles, retail applications, and medical imaging analysis.
How to label Images for Semantic Segmentation?
Although semantic segmentation is a tedious and labor-intensive process of annotating images at a pixel level, it has the advantage of offering accurate computer vision. Keep in mind that the tools and techniques you choose can make a lot of difference to achieve a reliable pixel-accurate image annotation for machine learning.
Outlining the shapes of the objects using pen tools speeds up the process of semantic segmentation. The pen tool supports freehand drawing, straight lines, and erasing inaccurate outlines. Once the images are labeled through semantic segmentation, the tags can be saved or exported as training datasets for AI or ML models.
Semantic segmentation has the following technical advantages over other image annotation techniques to obtain pixel-level accuracy.
Nested Classifications for Instance Segmentation
Using instance segmentation, you can classify objects nested within each other. You can also create separate regions for entities by using the list of objects or instances. Using a pen tool, you can easily edit the objects to add or remove parts from the image. One of the popular uses of instance classification is annotating a group photograph. Nested classification is made possible when you tweak the editor of your computer using the annotation tool.
Bordering in Semantic Segmentation
Whenever you draw a border of an object in the given image, it is a prerequisite to share a border between the objects. When you outline a new object, and the border overlaps with the existing outline of an object, the new boundary will be shared. Sharing borders is especially suitable when you start labeling entities from the background. In a few instances, you may start drawing the foreground objects and trace the background objects without messing up the border created earlier.
Brightness and Contrasting of Objects
In semantic segmentation, the objects in the image are shaded with specific colors. In a few cases, elements in the nighttime or dark areas of an image are hard to differentiate from one another. Semantic annotation allows you to adjust the brightness and contrast level in the object to increase visibility.
Zooming and Panning Images
In the semantic annotation technique, you can also use zoom and pan features to annotate the images accurately. You can zoom in on the image elements to annotate and subsequently pan out to verify if the annotation is complete.
Four quick wins from pixel implementation:
Accurate annotations: The more precise the annotation, the higher the quality of AI or ML models. When the images are annotated thoroughly, it serves as the parent datasets to train the learning algorithms and thus improve the self-learning features.
Avert unnecessary loops: Pixel accurate annotation lets you annotate objects in a single session.
Accurate models: Pixel-perfect annotation helps you uncover missing pixels and fully annotate the images for a better AI model.
Speedy process: Though pixel level annotation is time-consuming, accurate annotations speed up the overall performance of AI-enabled applications.
Summary
Image annotation is the optimal foundation of machine learning applications that you frequently encounter. Pixel accuracy is essential for AI and ML models to understand the images in a real-time environment. Although these training datasets are accurate when created, they need to be updated from time to time.
PreludeSys is a reliable and affordable data annotation service provider that offers end-to-end pixel-accurate image annotation for AI and ML models. Because high-quality training datasets are an essential component in the development of healthcare, agriculture, robotics, aerial imagery, and autonomous machines, it is important to partner with experienced experts.
For more details, contact our domain experts to understand the many advantages of image annotation.
