Image annotation is the most important integral part of machine learning development. Actually, it is the process that helps the model learn from data and predict in the right way. And in visual perception based AI model, image annotation plays an important role in detecting the objects visible in the natural environment helping algorithms learn from object detection, recognition and segmentation.
Importance of Image Annotation
If you are working on speech, voice or language based NLP or NLU AI model you need annotated texts, speech, audio or annotated data to train the algorithm. But for computer vision based machine learning model you need annotated images, containing the objects labeled for machine detection.
Hence, image annotation plays an important role in detecting such objects in various scenarios. But in present world image annotation became more important with more significant roles in object detection with additional features and other abilities in various real-life scenarios. So, right here we will discuss more about the importance of the image annotation in real life.
Detection of Object of Interest
Making the objects detectable to machines is the most important part of image annotation. And computer vision based AI models like Autonomous vehicles, robots and drones need huge amount of machine learning training data created through image annotation. Bounding box is one of the most popular image annotation technique used to detect the various types of objects.
Classification of Various Objects
Apart from object detection, image annotation also classify the objects visible in the natural environment. Actually, there are different types of objects visible in an image and it becomes impossible for a machine to classify them unless is annotated with specific image annotation technique. If in the image animal and humans both are visible, then image annotation can classify such objects and making easier for the machines to detect and classify the objects from different species.
Recognizing the Different Objects
In the present world, another importance of image annotation is making the objects recognizable to machines through computer vision. Actually, there are different types of objects and recognizing them is important to differentiate them from each other with precise classification at ground level. And semantic segmentation is the most suitable technique helps to classify the objects in a single class making easier for machines to differentiate between various types of objects.
For Supervised Machine Learning
In supervised machine learning labeled data is required for AI models. Yes the training data should be properly labeled to make the algorithms easily recognize and understand the scenarios by detecting and recognizing the objects with extra precision. In supervised machine learning, two types of algorithms is used. First for classification which helps to classify the into desired categories. And second regression that helps to predict a value based on past data.
Cogito offers the best platform or image annotation services. And for visual perception based AI model or computer vision machine learning model training it is providing the best quality training data sets at most competitive pricing. It can annotate the objects with bounding box annotation, semantic annotation, landmark annotation, 3D Cuboid annotation, polygon annotation and polyline annotation.
Thanks for posting, keep adding more valuable content.