Artificial Intelligence (AI) and Machine Learning (ML) are getting more interest by computer engineers who bring to light this progressive technology implement into untapped fields or improve the performance and efficiency of existing fields.
And availability of machine learning training data is the crucial point to improve the AI performance. While image annotation is the techniques used to create training data for visual perception model developed on the principles of AI and ML.
So, you need to first understand the importance of image annotation in AI and ML, so that you can further explore untouched fields where AI is needed.
Actually, to make machines perceive objects in their natural surroundings you need annotated images that you can use to train the ML algorithm learn and predict.
In machine learning or AI, you need to train the machine detect the various types of objects visible in the natural environment. Self-driving cars, robots and autonomous flying machines cannot detect such objects, unless trained with a certain process. And annotated images makes object of interest detectable to machines.
Bounding box image annotation is the precise technique, makes different types of objects recognizable to machines through computer vision. It can be used to develop AI-enabled models for automotive, retail and various other fields.
Object detection is the not only the objective of image annotation, instead in while classifying the objects it works in the same manner. Actually, there could be different types of objects in an image and it becomes impossible for a machine to classify them.
For an example, there is dog and man in the same image, so both have to be classified as different objects and recognize the similar objects when shown to AI models in the real-life use. So, image annotation is the method classify such objects. However, the image annotation is used for computer vision to train the AI models.
Also Read: What Is Computer Vision: How It Works in Machine Learning and AI
Similarly, when there are different types of objects in a single image, it becomes difficult to recognize the same, that because of similar dimension. In such cases object recognition and classification both required 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 computer vision this technique objects can be configured with nested classifications, which is also very handy for similar properties.
Also Read: How To Label Data For Semantic Segmentation Deep Learning Models
Another considerable important of image annotation is it helps to create a label data sets for machine learning and AI. And for supervised machine learning, annotated images is must helps algorithms easily detect and classify the objects.
Actually, 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.
Another most important objective of image annotation is while developing and AI or ML model it helps to validate the models to test for the accurate prediction. Annotated images are used to check whether model is able to detect, recognize and classify the objects precisely and predict the same with accuracy.
In this process, machine learning model is validated by the experienced annotators and engineers. And if annotated images will be not available, how model will detect the objects, and this process also helps to evaluate the quality of image annotation services. As, if images are not annotated properly, algorithm will not able to relate the same from its database or past experienced gained from machine learning.
Also Read: How to Validate Machine Learning Models: ML Model Validation Methods
Hence, image annotation has significant role in machine learning and AI development. But quality of machine learning training data is the another aspect, should be considered to ensure your model is getting right training. As, incorrect annotated images will misguide the machine due to inaccurate feed of data into algorithm.
Cogito is the one of the best image annotation companies providing the high-quality training data for machine learning and AI developments.
It is offering data and image annotation services for wide-ranging industries with all leading techniques making each object recognizable to machines with pixel-wise annotation at lowest cost.
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