Application of Machine Learning in Image Processing!
Machine learning is one of the most widely implemented AI techniques across industries, academia, and individuals alike. The ML technique enables businesses to automate business processes, improve data accessibility, maximize computational power and drive meaningful results from data analytics.
When it comes to image data, machine learning algorithms are widely used to read, process and interpret images, mimicking human brains. In fact, powerful machine learning algorithms are able to identify hidden patterns and anomalies in images, which otherwise may not be identified. From face recognition technology used in smartphones to self-driving cars, automating manual processes and everything in between, machine learning techniques are enabling companies to explore new business frontiers across industries.
Here in this blog, we will specifically be looking at the basics of machine learning technology for image processing and see how the technique works to read, process and interpret image data.
Introduction to Image Processing
Image processing refers to a computer technology that is concerned with processing, analyzing, and interpreting images to extract insightful information.
Given the cross-industry applications of the technology, image processing has evolved significantly over the years, enhancing the capabilities and accuracy of the technology. Today, hundreds of businesses across different sectors utilize image processing techniques for various applications, including pattern recognition, image classification, target detection, image visualization, segmentation, anomaly detection, and many more.
While there are various image processing techniques, these can broadly be classified as two, including analogue and digital image processing. The analogue process is mainly implemented on hard copies, including printout photos, etc. Digital image processing, on the other hand, is used to read, process, analyze, and interpret digital images through computers. The output of digital image processing techniques is usually extracted features like masks, characteristics and others on raster or vector format.
The advancement of machine learning processing techniques also increases businesses’ capacity to leverage the technology across even more fields and more powerful purposes. Here are some of the leading use cases of ML image processing techniques:
– Medical Imaging: One of the top use cases of image processing is in the field of medicine, where the technology is widely implemented to record and interpret medical imaging like x-rays, ultrasounds, etc. for faster and more accurate diagnosis
Working of Machine Learning Image Processing
In essence, machine learning algorithms are developed to learn specific tasks over time from data. This means that there are specialized machine learning algorithms developed for individual tasks (even though the same algorithms can be trained for slightly different jobs as well). What is Information Security Management
Now, one limitation of machine learning algorithms is the requirement of large structured datasets. These algorithms require high-quality structured data for training and to yield accurate results. Thus, it’s essential to have well-processed and annotated training data for machine learning image processing. That’s where computer vision (another branch of AI) comes to the rescue. Computer vision technology is concerned with empowering machines to read, process, and interpret image data like humans. The technology can help the process, transform and manipulate digital images, which subsequently can be used to train machine learning algorithms.
Let’s understand this with an interactive example. Consider you want to develop a machine-learning algorithm to identify cats and dogs in an image. To be able to develop such an algorithm, you will need to have a large quantity of cat and dog images, which you will preprocess using computer vision. The processing steps for extraction of cats and dogs will include the following steps:
- Transforming all the images into one similar format
- Masking the unnecessary regions on images
- Training the machine learning algorithm to learn from the prepared data
At present, there are various machine learning algorithms, including decision trees, nearest neighbour, neural networks (CNN), and others, that are being widely used in the image processing industry. These algorithms can be trained on the dataset for pattern recognition under specific supervision (supervised classification) or used for self-training (unsupervised classification). However, the results of supervised classification are much accurate as compared to unsupervised classification techniques.
Well, in this blog, we have gone through the fundamentals of machine learning techniques in image processing. Today, machine learning has become a crucial technology across various industries, including medical, geo-informatics, GIS, agriculture, and others, helping professionals automate processes and extract/identify patterns from images. Some of the more popular languages used for building machine learning algorithms include Python, R, C++, C# and other open source libraries. The key to achieving higher accuracy for image interpretation using machine learning algorithms lies with the quality of the training dataset. A sizeable structured dataset is crucial to train algorithms for specialized tasks and achieve superior accuracy from the algorithms.