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 extent of algorithms up to which 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 concerns the concerned authorities, like in case of robbery, flooding, or traffic jams, etc. (How Data Is Used in Smart Cities – Project Sherpand)

PCB Defect Detection with Data Labelling

Figure 1. PCB Defect Detection with Data Labeling (Huang et al., 2020)

Up till now a discussion on machine learning and data labeling has been briefed in this article. In this subsection, a brief discussion of how data labeling works in machine learning will be presented.

Data Labeling

The process of data labeling in machine learning is a system in which raw data has been obtained by the model. This raw data is in the form of images, texts, and some hidden meaning images. With the help of already placed data in the model, the model with the help of artificial intelligence, deep learning, machine learning, and CNN trained itself and made decisions, and labeled the unlabeled data that has been given to the model. This data labeling can be done manually by adding manual labels to an image and with the help of image processing, the system can label other images. In that case, the images are of the same size, dimensions, and pixels. When machine learning involves data labeling, it automates the model; hence, one can now put random images of the same grouping and the model can now label the images all by itself (What Is Data Labeling?, 2019).

The data labeling automation is the prediction mechanism. The prediction becomes strong when the data provided is well balanced; for example, when the data have all the rotated images, translated images, high pixel, and low pixel images. The more the data is well balanced the more automation can be achieved. It also depends on the algorithm that the developer has been presented. More accuracy in this work can be achieved when the algorithm that has been developed by the user can show the pathway to the model, and having more target-oriented (The Ultimate Guide to Data Labeling for Machine Learning, 2019). Another online article (How to Label Text for Sentiment Analysis — Good Practices | by Lima Vallantin | Towards Data Science, 2020) puts emphasis on setting the rules through fuzzy logic. In this way, the model only labels useful information from the image and leaves the rest part of the image.

There is also confusion for new researchers in the field of machine learning. In machine learning there are basically two types of data one is labeled and another one is unlabeled (2.1 What is the difference between Labelled and Unlabelled Data? – Grokking Machine Learning, 2020). From the name of both the data and previous discussion, we can observe the labeled data is the one with the help of which we can observe the name tag in an image. In unlabeled data, there are only images that are useful but there is no name tag on them. In the following figure, a common example has been shown which helps to improve the concept much better.

Difference Between Labeled and Unlabeled Data

Figure 2. Difference between Labeled and Unlabeled Data (2.1 What is the difference between Labeled and Unlabeled Data? – Grokking Machine Learning)2020)

In literature, there are different names are enlisted that go with machine learning. These are supervised, unsupervised, and semi-supervised learning. Among these, there are only semi-supervised learning which can process both labeled and unlabeled data. Apart from semi-supervised learning, there are other machine learning tools that can process both. These are supervised GANs, adversarial training, and semi-supervised GANs. From this, we can conclude that there are only limited tools that can process both. It is better to have labeled data so that there are more tools that can process our data and hence no time should have been wasted (How to Use Unlabeled Data in Machine Learning2018).

The main aim behind labeling the data is with the help of labeling we can easily use various machine learning algorithms. The most common of them is supervised machine learning. When our data that has been uploaded in the system or the model is labeled, we can use supervised machine learning which has the following benefits (How to Use Unlabeled Data in Machine Learning2019).

  • With the help of supervised machine learning, we can optimize our results and performance of the model as it works on the previous experiences as well.
  • Lesser computational power is required but better results can achieve.
  • Mostly only available machine learning platforms work on supervised machine learning parameters, so supervised machine learning is important.
  • With the help of supervised machine learning, we can solve real-world problems with greater ease.

References

  • 2.1 What Is the Difference Between Labeled and Unlabeled Data? – Grokking Machine Learning. (n.d.). Retrieved February 25, 2022
  • Bengio, Y., Courville, A., & Vincent, P. (2013). Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828.
  • How Data is used in Smart Cities – Project Sherpa. (n.d.). Retrieved February 25, 2022
  • How to label text for sentiment analysis — good practices | by Lima Vallantin | Towards Data Science. (n.d.). Retrieved February 25, 2022
  • How to Use Unlabeled Data in Machine Learning. (n.d.). Retrieved February 25, 2022
  • Huang, W., Wei, P., Zhang, M., & Liu, H. (2020). HRIPCB: a challenging dataset for PCB defects detection and classification. The Journal of Engineering, 2020(13), 303–309.
  • Machine Learning: What it is and why it matters | SAS. (n.d.). Retrieved February 25, 2022
  • The Ultimate Guide to Data Labeling for Machine Learning. (n.d.). Retrieved February 25, 2022
  • What Is Data Labeling? (n.d.). Retrieved February 25, 2022

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