Machine Learning in Automated Image Labeling

What Is Machine Learning?

A branch of artificial intelligence with the help of which data can be obtained from a system, processed using automated analytical building models is known as machine learning (Machine Learning: What It Is and Why It Matters | SAS, 2019). The basic aim behind the introduction of machine learning in an automated system is so that the machine can take decisions all by itself using data, patterns, and surrounding parameters without any human interventions. By another definition (Bengio et al., 2013) machine learning, is basically an understanding of algorithms up to which extent the machine can learn algorithms, draw conclusions, and act accordingly. 

While talking about image labeling through machine learning, this task is not difficult in the present technological world. There are several examples of automated image labeling with the help of machine learning. The most common of them are PCB defect detection (Huang et al., 2020), in which the model with the help of a predefined data set label all the false part of a PCB which helps the PCB manufacturing industry in the quality control section. In the same way, there are several safe city projects having CCTV cameras that work on this principle. They captured the high-quality images and labeled different things like cars, humans, and animals which makes them easy for authorities to understand the situation and even having some emergency trigger point with the help of which the system automatically warns the concerned authorities, like in case of robbery, flooding, or traffic jams, etc. (How Data Is Used in Smart Cities – Project Sherpa, n.d.)

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