To understand a car or a jet, one has to first grasp the concept of aerodynamics. It is hard to appreciate the brilliance of quantum computing without first comprehending the wave theory. Similarly, if you are thinking about investing in AI for Image Analytics, it would help to sneak under its hood and find out just how this magic works.

Does an AI tool simply pull intelligent answers out of some hat? Or does it have a fundamental lever that makes it all possible? Turns out, the magic sauce is all about the process of recognizing patterns – in a fast, clever and creative way.


What is Pattern Recognition?

Pattern recognition drives the core of any image analytics solution. These patterns are what the models smell. These patterns are the very ground-work that lead to probabilities and predictions that any algorithm churns out.

So, what is pattern recognition, after all?

Basically, the automated process of finding threads and connections in the visual data construes the underpinning of pattern recognition. It can be done either through knowledge that already exists or through new cognitive routes. It can also be explained as the discovery and deductions of regularities in data by using automation and algorithms.

It involves some core areas:

  1. Classification of data
  2. Categorization of data
  3. Labeling of data
  4. Assignment of features that lead to measurement and quantification of objects
  5. Converting features into vectors which represent specific attributes of the objects
  6. Training models
  7. Clustering
  8. Extraction
  9. Translation and reporting
  10. Decision-enablement

When we think of patterns, we can think of the description that helps to create an object, its abstraction or its specific behavior. The data can be an image, some text, a piece of sound, a sentiment or anything else. The data is gathered, cleaned of noise, examined for similarities, grouped as per criteria, analyzed and translated into patterns.


How does Pattern Recognition work?

With the help of training data (ex.-images of various customers entering a retail store), a model can be taught to recognize specific features and patterns. This can be the emotional state of these faces or the demographic profiling of these faces. The model starts learning, and re-learning as it begins to recognize some vectors (like expression, eye position, ear structure, nose placement, smile or absence of it) and collates them into a verifiable pattern. This is an area where one can use the tools of statistical analysis, probability, geometry, signal processing, machine learning, deep learning, computation etc. to devise inferences from data.

It works in these major ways:

  • Statistical – identification of where the object belongs
  • Structural – identification of a complex relationship between elements
  • Template matching – Alignment of data to prescribed templates
  • Explorative – Finding commonalities in data
  • Descriptive – Using a specific manner to classify the commonalities
  • Supervised to Unsupervised – Using humans to a certain degree or not using their opinion or support at all

Any good pattern recognition system should have the following characteristics:

  1. Accuracy
  2. Consistency
  3. Speed
  4. Angle-adaptability
  5. Ability to pick hidden data
  6. Automaticity

21 Use-Cases and Applications of Pattern Recognition

The applications of pattern recognition are numerous and enterprises have hardly begun to scratch the surface of intelligent patterns. They can help to:

  1. Detect false positives or negatives in enforcement or surveillance scenarios
  2. Create alerts and red flags that have no precedent but can, still, be quite significant
  3. Find correlation between one problem and another – like between people entering a public area at a certain time and the incidence of thefts in that area
  4. Enable better depth in computer vision and imaging areas (especially medical imaging)
  5. Equip discovery-related applications like seismic patterns for weather departments, or radar signals for mining exploration and safety
  6. Expand the accuracy and impact of CAD (Computer Aided Diagnosis) – with the help of images obtained through ECG, X-ray etc. (Ex. – in December 2019, GE launched 30 imaging applications in its radiology department)
  7. Enable forecasting – in stock markets, for marketers based on audience research, for climate and disaster-related events
  8. Shape impactful smart cities through robotic surveillance and fast services
  9. Facilitate AI-based visual search engines
  10. Create actionable sentiment analysis insights for marketing campaigns
  11. Empower administration and law enforcement authorities with quick support and cues – through optical data
  12. Help manufacturing personnel in finding machines, doing preventive maintenance, and resolving accidents through sensor-generated data
  13. Help improve the process of quality management by picking defective products and identifying non-defective products early in the cycle
  14. Make shopping fast and personalized for customers by letting them take pictures of real products around them, put customization inputs and order on a platform
  15. Help in elevating customer experience and areas like sales-support, inventory management and restocking by using in-store image-based patterns
  16. Get a 360-degree grip on customer and employee experiences (Ex- Coca Cola’s implementation of a comprehensive view in April 2020)
  17. Support and accelerate tasks like plagiarism-checking
  18. Help agriculture applications in enhancing yield and removing guesswork through fast and accurate information from soil maps
  19. Achieve better safety and ease in driverless cars and automotive services
  20. Create new services for banks and financial institutions by using facial-recognition for payments (Ex- Caixa bank in Spain that started deploying this for touchless withdrawals in its ATMs in June 2020)
  21. Remove friction and supervision from attendance systems and retail-store support

Models that can be relied upon are built with some innate qualities that only a few players in the industry can provide.

  • These models are good at learning
  • They can adapt quickly and with higher levels of accuracy with every iteration
  • They are capable to optimize both kinds of data – training dataset and testing dataset
  • They are complex enough but not complicated or confusing
  • They can work with the scale and quality of datasets available
  • They do not let decision-makers grapple with the opacity of model-functioning
  • They are flexible enough to identify and understand images even if they are rotated, magnified, compressed or blurred
  • They are robust and dependable in all cases – feature selection, supervised learning and unsupervised learning
  • They can help an enterprise get past through the tough legwork of APIs, hardware, algorithm design and coding
  • They can help to reduce initial development costs of tools like CNN (Convoluted Neural Networks), gesture recognition and facial recognition
  • They can expand the returns and impact of the solution by adding creative value and expertise


Pattern Recognition in a digital world with Staqo

Today, the global image recognition market is growing at a profound pace – from $23.82 billion in 2019 to a projected size of $86.32 billion by 2027. Other estimates put this trajectory from $26 billion in 2020 to $53 billion by 2025.

One needs a good set of experts and experience to make sure that the image analytics solution can rise to the expectations and challenges mentioned above. This needs the proficiency and domain-excellence of a player which packs more than tools. If an enterprise approaches AI-based image analytics through a partner that is capable, that brings versatile experience, is passionate about this area and has the right team of brains – then this enterprise can eke out actual magic out of its investments.

The car will inevitably turn out great and reliable when you hire an aerodynamics’ engineer who understands and loves cars. Not a mechanic or a car dealer. So, make sure you join hands with a team that understands pattern recognition for all its possibilities and challenges. That’s the right place to begin. The rest of the dots will add up beautifully.