Table of Contents
- What Is a Pattern Recognition System: Definition and Types
- Pattern Recognition Algorithms: How Do They Work?
- Why Should We Care About Pattern Recognition Systems?
- Final Thoughts on Pattern Recognition and Machines’ Ability to Develop Human Skills
Recognizing patterns and, thus, distinguishing different objects and events in the world is a natural ability for humans. It’s the way human intelligence works, and something that machines have been trying to adopt from humans for a while.
Pattern recognition is arguably the most fundamental cognitive skill for humans that helps us perform high-level decision-making, a key determinant of our survival and evolution in complex environments. Machines, however, pursue this skill of recognizing complex patterns in data to solve challenging issues in machine learning. This helps humans achieve the ultimate success in automating manual tasks and handing most of them over to advanced technologies. You’ve probably spotted them often in everyday life.
Do you use Apple’s Face ID or fingerprint identification to unlock your phone? Or maybe you’ve been searching for a new song using Shazam? Are you the owner of a self-driving vehicle that recognizes objects on the street by itself? All of these are prime examples of sophisticated pattern recognition systems that are widely applied across different industries and in our lives. Pattern recognition is an interesting approach to applying ML algorithms for a myriad of applications in the real world.
Keep on reading to find out more about how machines are already one step closer to recognizing, identifying, and classifying objects in the surrounding world as humans do with pattern recognition!
What Is a Pattern Recognition System: Definition and Types
Patterns are recurring trends in various types of data that may be observed physically or mathematically using statistical methods and complex algorithms. While humans are innately capable of recognizing patterns, machines can do so by following a set of complex processes and by using the necessary inputs.
Machine learning and pattern recognition are frequently used interchangeably. Is that correct? Well, not exactly. Pattern recognition is one of the methods of how ML can be used to solve complex issues in the field. ML algorithms help these systems identify familiar patterns and teach machines to observe the environment and distinguish objects in it. Machine learning underpins the pattern recognition systems to allow them to analyze incoming data, identify patterns, and make logical inferences.
Regardless of the type of data you have, our team at Label Your Data can always assist you with annotating and preparing it for complicated systems like pattern recognition. Send us your data, and we'll make a free pilot for you!
Pattern recognition is a mature but constantly evolving field, the foundation for advances in computer vision (CV), image processing, and NLP tasks (Natural Language Processing). It’s a data analysis technique, which uses ML algorithms to find patterns and regularities in data in the most accurate and fastest way possible. Pattern recognition is an automatic process based on different types of data inputs, including images, videos, text, audio, digits and other identifiable qualities.
There are different paradigms used to denote the practical issues that pattern recognition deals with. However, the most important ones are statistical and syntactic pattern recognition. The issues include noisy data and uncertainty that are efficiently addressed with statistics and probability.
— Statistical Pattern Recognition
This pattern recognition method uses statistical historical data to learn from examples. The system collects observations, processes them, and learns to generalize and apply these principles to new observations. It’s a statistical method for analyzing data measurements to extract information and generate logical decisions.
— Syntactic Pattern Recognition
Also known as structural pattern recognition, this type of recognition works with primitives, aka simpler subpatterns. The identified pattern itself is characterized based on the connections between the primitives. Here, each object is interpreted by a variable-cardinality set of symbolic, nominal features.
— Neural Pattern Recognition
As the name suggests, this type of pattern recognition uses ANNs, which we’ll discuss in more detail later. Neural networks have the ability to solve complicated nonlinear input-output relationships and adapt to the data.
— Template-Matching Pattern Recognition
This pattern recognition technique is used to build simple models that can establish similarities in two images and store the matched patterns in the form of templates. However, this approach is ineffective at detecting distorted patterns.
— Hybrid Pattern Recognition
As it usually happens in AI, there’s no one-size-fits-it-all algorithm that can solve all the cases in pattern recognition. Thus, sometimes it makes sense to go for a hybrid approach and build a model based on multiple classifiers to recognize patterns in data. Each classifier is trained using feature spaces.
Pattern Recognition Algorithms: How Do They Work?
ML algorithms used for pattern recognition typically seek to produce a viable solution for all potential inputs and perform matching of the inputs, considering their statistical volatility. The type of label output, ML method (supervised or unsupervised), and the type of algorithm itself (statistical or non-statistical) are all factors that influence the pattern recognition algorithms.
Most pattern recognition systems are data-intensive, so these systems’ accuracy is directly dependent on the quantity and quality of the training data. In most cases, the ML systems are trained on labeled training data (supervised learning). However, other pattern recognition algorithms can also be used to identify unknown patterns in unlabeled data (unsupervised learning).
Classification Algorithms (Supervised ML)
- Linear discriminant analysis
- Quadratic discriminant analysis
- Maximum entropy classifier (logistic regression, multinomial logistic regression)
- Decision trees, decision lists, Random Forest classifier
- Naive Bayes classifier
- Kernel estimation and K-nearest-neighbor algorithms
- Neural networks
- Support vector machines
- Gene expression programming
Clustering Algorithms (Unsupervised ML)
- Deep learning methods
- Categorical mixture models
- K-means clustering
- Hierarchical clustering
- Correlation clustering
- Kernel principal component analysis (Kernel PCA), TSNE
Pattern Recognition Process Overview
A pattern itself is represented by a physical object that one can observe or an abstract concept. In general, a pattern is a term for how we characterize an item (e.g., describing a flower). Patterns have different classes as well (e.g., different species of the same glower). The important point here is that pattern classification requires a proper selection of attributes and representation of patterns.
Let’s take a closer look at this process:
- A pattern recognition system obtains input data from the real world by using sensors. Such a system can handle any type of data.
- The algorithm used for pattern recognition starts to pre-process the data and isolate the sensed objects (aka segmentation).
- Throughout the pre-processing stage, it’s crucial to filter out the noise from the dataset.
- After that, it’s time to move on to processing data: extracting features by identifying certain common characteristics that appear across several data samples and sending them for further classification.
- Categorizing the sensed objects in groups or cases (aka classification).
- The final, post-processing phase: getting the results and making the final decisions.
Why Should We Care About Pattern Recognition Systems?
In ML, pattern recognition techniques can handle various classification issues, and they can be used in almost every industry today. However, there are three crucial aspects to consider in order to tackle these problems and reap the benefits of pattern recognition: the volume of data, the chosen method, the designer, and the user. And it’s crucial to build a system that is capable of working with large data to get the most accurate results.
What can one expect from pattern recognition in AI?
- Automatically detect familiar patterns with speed, accuracy, and precision
- Classify unfamiliar objects
- Find patterns and objects that are partly hidden
- Identify objects from various angles
- Restore patterns in instances of missing data
As the informational labeling process, pattern recognition is the technological imitation of the decision-making process of humans. Therefore, the main goal of the pattern recognition process is to elucidate the complex mechanism of decision-making and automate the process using algorithms and computers. This way, pattern recognition becomes an underlying idea of most ML systems created to teach machines to make decisions without any human involvement.
However, keep in mind that ML systems are data-hungry, thus it’s essential to prepare your data before dealing with such complicated systems. Contact our team to have your data annotated and ready for your AI project!
Applications of Pattern Recognition Algorithms
As mentioned earlier, pattern recognition is a highly popular way of using ML capabilities to solve decision-making issues in most of the modern industries:
CV & Image Pattern Recognition
Pattern recognition is a perfect fit for image processing and segmentation. We’ve previously discussed how ML algorithms have already developed intelligence akin to that of humans, allowing machines to be able to analyze the visual content.
Natural Language Processing
Pattern recognition algorithms used in NLP are great for creating robust software systems, which can be further applied for work with textual data in the computer and communications sector.
It’s not only about the visual world, machines can get a sense of music, too. We have a more in-depth discussion of this topic, if you are interested. But, in short, pattern recognition paradigms can treat large datasets simultaneously and use competitive algorithms to enhance speech and linguistics recognition.
The seismic analysis relies on statistical pattern recognition techniques that find and analyze sequential patterns of seismic array recordings. Different types of seismic analysis models are used for identifying, imaging, and interpreting patterns in seismic data.
The biometric market is dominated by fingerprint recognition technologies. To achieve fingerprint matching, a variety of recognition methods have been explored, with pattern recognition approaches being the most popular.
Finance has been one of our heated discussions lately, and pattern recognition has something to do with it as well. By analyzing the market data, this system can provide crucial insights to prevent financial crashes and large-scale financial risks. Pattern recognition is also used in investment and stock market predictions.
This is only a small fraction of all the applications of pattern recognition these days. This technology has gone far beyond its capabilities in AI.
Pattern Recognition in Artificial Neural Networks
Artificial neural networks (ANNs), aka neural networks, is another subset of deep learning that was inspired by the way the human brain works. Neural networks are based on the idea of imitating the functioning of the human brain, using neurons that mimic human decision-making, to find hidden patterns in datasets. How are ANNs used in pattern recognition?
Neural networks in pattern recognition are based on data acquisition and remote sensing. When ANNs are applied to a pattern recognition system, they require all steps from preprocessing (removing disturbances in data, separating patterns from an object, feature extraction) to post-processing (decision-making regarding pattern recognition). Typically, a convolutional neural network (CNN) is used as the main ANN model for the specific pattern recognition task.
ANNs used in pattern recognition systems cover a broad scope of areas, such as business, communication, biometrics, automation, speech recognition and classification, smell recognition, chip manufacturing, and the financial sector. Among other applications of ANNs are handwriting recognition, bioinformatics, biotechnology, data mining, crime detection, military, credit fraud detection, medical diagnosis, agriculture, etc.
Final Thoughts on Pattern Recognition and Machines’ Ability to Develop Human Skills
Finding the golden mean between the machines’ capabilities and human intelligence has always been the primary objective in AI. Pattern recognition is one of the examples of how machines can develop human-like skills and interpret the surrounding world, yet not without a thorough human control.
A complex, data-intensive task of pattern recognition can be used to solve pressing analytical problems with automation. This process proves to be a crucial ML technique that brings the recognition of data regularities and patterns to the next level. A vast number of pattern recognition applications and the real-life challenges addressed using training data, testing data, and classifiers, prove this point quite well.
And it seems that pattern recognition in artificial intelligence is soon to become an even smarter solution supporting many digital technologies and advances in robotics, automation, and CV. We at Label Your Data can’t wait to enjoy the upcoming benefits of pattern recognition and are all set to annotate as much data as possible for such sophisticated systems!
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