Image annotation is a process that is used to label data of images, videos and texts. These are predetermined labels that are placed by a computer vision professional or a machine learning engineer. It is done to render computer vision model data on the objects that are in the images.
ML algorithm uses the image annotation to learn and identify similar patterns of objects when provided with a brand new data. It is done on the basis of project requirements because different projects require different methods and types of image annotation services.
Let us have a detailed look at multiple types of solutions to annotate image online.
Polygon Annotation
It is used for objects that have irregular shapes and sizes and hence, can’t fit perfectly in a box. Polygon annotation is done to achieve more accurate annotation for oddly shaped items including trees, houses, fruits, landmarks, and more. It requires an advanced level of precision from the image annotator to achieve perfect results for AI and ML algorithms.
Line Annotation
Line annotation is used to annotate splines and lines used to create boundaries in an image’s region. It is majorly used for a thin or small section where you can’t annotate the image with a bounding box efficiently. In contrast to the bounding box annotation solution, it enables the engineer in decreasing the extra noise and white space from the image. Line annotation is used largely to label images for autonomous automobiles including self driven cars.
Bounding Box
The bounding box is one the largest used annotation practices. It is commonly used to annotate images with simple objects. All you need is image annotation labelers to create a box all around the corners of the specific objects located within the picture. 2D bounding boxes are mostly leveraged for localization, object classification, and detection by many organizations and industries like eCommerce, healthcare, retail, and more.
Point Annotation
Point annotation is conducted to make sure that correct and reliable plotting of key points are located at specific points on the image. Normally, it is used to help sentiment analysis or to enable facial recognition algorithms. Through recognizing and following through the moves of marked surfaces on the face’s expression, the machine learning algorithm can easily find out emotions through predictive reading tech.
Semantic Segmentation
Semantic segmentation is majorly used to segregate the pictures into multiple sections and classify images’ all pixels in the segments along with related class labels of what it reflects including car, traffic light, lamp post, pedestrian, and more. It makes sure that the ML algorithm always has a solid understanding of all the pixels of the image.
It is majorly leveraged to detect and also localize specified objects present in the image. This application of robust and granular intelligent understanding of images is normally used in multiple applications like autonomous vehicles. Because self-driving vehicles require a greater understanding of the surrounding environments to ensure a safer driving. It is also used in the agriculture industry where semantic segmentation is used to analyze the condition of crop fields to detect abnormal growth and pests.
In Summary
With a rapidly advancing computer vision technology, the ways to develop training data are also evolving at a faster pace. Image annotation is also one of the most important jobs to achieve flawless computer vision efficiently.
Hence, it is crucial to have the right image annotation partner who can deliver accurately annotated images round the clock. Companies like Merakee Labs offer high quality image annotation solutions at cost-efficient prices. You can also do your own online research to find the right image annotation service.
