What are Patterns?

Pattern recognition is the process of identifying and recognizing a pattern from a data set. When performed by humans naturally, it can be considered a cognitive process in which our brains ingest certain information and match it with existing or preconceived information to identify it as a familiar pattern. Our body also carries out pattern recognition with the help of pattern recognition receptors (PRR) in our body.

In terms of computing technologies, pattern recognition can be defined as a similar task where a computing system uses stored data, compares it with any input data, and identifies it based on shared common characteristics. Thus, a pattern recognition algorithm can help a computing system to identify similar images, such as a ball or a bat, based on previously stored information on how a ball or a bat looks.

Pattern recognition is closely related to but not the same as machine learning. It can be termed a derivative of machine learning as it essentially identifies common dataset characteristics. It can even uncover hidden patterns among seemingly unrelated objects, thus helping analysts establish a correlation between two datasets. It involves using data analysis methods such as classification, clustering, and statistical calculations to identify familiar characteristics from two data sets.

Pattern recognition implies some pattern to uncover from a given set of data. So, what is a pattern? A pattern can be any quantifiable metric or characteristic represented as a vector. It can be of any type of data, such as speech, binary data, text, images, and so on.

How does pattern recognition work?

Various stages are involved in a pattern recognition task that can be performed in a continuous cycle to keep improving the results of the pattern recognition algorithm. Here are the basic steps involved:

  • Data acquisition and preprocessing

    Input data is fed to the pattern recognition algorithm and stored as a recognizable, unique characteristic set. In this stage, the input data is cleaned and processed to be free from noise and redundancy.

  • Data representation

    In this stage, the input data is analyzed and broken down into a vector representation that can make it easy to compare with more incoming data. The features extracted from the data are segmented and stored as recognizable patterns.

  • Decision making

    The patterns identified from the previous step are fed to a data model that can compare the extracted features to any incoming dataset and identify similar patterns. This stage can use different algorithms such as classification, clustering, value prediction, and more, depending on the use case, to arrive at the final decision-making on pattern recognition.

    The stages of data pattern recognition can be repeated continuously to keep adding more patterns to the database, which can then be used to compare with input data to match and identify the already discovered patterns. Thus there can be two parts to a pattern recognition model.

  • Descriptive pattern recognition

    This type of pattern recognition mainly uses a supervised classification technique where patterns are matched to an already predefined pattern class. For instance, from an image, different vehicles can be identified by comparing them to the features of predefined classes of cars and bikes. This type of pattern recognition can thus also be defined as a classification task where incoming data is classified into predefined classes by comparing and matching them.

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Text Recognition for Machine Learning

Types and variants

Multiple approaches can be used to create a pattern recognition model. Some of the major types of models are:

Statistical pattern recognition

This approach uses mathematical and statistical models to extract features and discover patterns from data. It also uses historical data points and previous observations to uncover hidden patterns. Data is represented in the form of a vector representation. This type of model is often used in developing AI-based tools for predicting financial forecasts, market predictions, and discovering market trends.

Syntactic Pattern recognition

This type of pattern recognition is used when the datasets cannot easily be converted to a vector representation. For instance, image data might contain several structural pieces of information which can be quite difficult to translate to a finite set of feature vectors. In these cases, syntactic pattern recognition can be used to break down information into hierarchical structures.

Neural Pattern recognition

Neural pattern recognition methods use neural networks that closely resemble how an actual brain works. This type of pattern recognition is the most widely used, given its versatility in dealing with different kinds of data types and unknown data. It is also capable of highly complex data sets.

Template matching

This technique uses predefined templates, which are matched with incoming data to recognize a pattern. It is often the technique used in computer vision applications such as medical imaging, robotic vision, traffic vehicle tracking, and more.

Pattern recognition tools

The most commonly used tools in pattern recognition technology are:

  • OpenCV for image data
  • Principal component analysis (PCA) for biometric geometric data
  • Support Vector machine
  • Scikit and similar libraries from Python
  • Deep learning models from Caffe and Theano

Pattern recognition technology applications

Here are some of the popular application areas of pattern recognition:

Stock market analysis and prediction

While it may not be quite possible to predict one’s future, it can be possible to predict what could happen next based on a pattern of events that lead to a similar conclusion. When coupled with market analysis data, this idea can be used to predict stock values in the market using pattern recognition. For instance, chart patterns observed from historical data can be used to predict the future values of stocks depending on the current trajectory of market conditions. Pattern recognition often combines deep learning algorithms and LSTM to help predict the stock market.

Target audience and user behavior research

When you want to introduce a new product or campaign to your audience base, it makes sense to get some forecast on how it could be received. Pattern recognition can help with user segmentation and analyze patterns of user behaviors to make better products and personalize user experience. Many OTT streaming apps and eCommerce applications have been using user preference patterns to suggest products that customers would find interesting.

Text recognition

Much of natural language processing and AI-based text generators depend on identifying patterns from speech and textual data. Applications like AI-based translators, grammar checkers, plagiarism checkers, and more use pattern recognition techniques to achieve the desired results. Text recognition is also part of optical character recognition, chatbots, and customer feedback analyses.

Image recognition

As mentioned earlier, image recognition is one of the foremost application areas for pattern recognition algorithms. It is extensively used in medical imaging to diagnose and identify anomalies from medical imaging data such as x-ray images, scanning reports, and so on. Pattern recognition, in combination with image processing, has also found use in advanced medical fields such as DNA sequencing and drug development. All computer vision systems use some form of pattern recognition to classify and identify objects from images. Image search is another set of applications that work on top of pattern recognition techniques.

Voice and speech recognition

Pattern recognition can also be applied to audio data enabling users to classify audio data, recognize speech information, convert speech to text, and vice versa. The automatic subtitle generation employed in many streaming services is one perfect example of effectively using pattern recognition on audio data. It is also extensively used to identify musical patterns and thus is a prominent technique to tackle copyright issues within the music industry.

Network and system security

AI-based security systems employed in the cloud and on-premise solutions can use pattern recognition to catch and take preventive actions to secure the systems. For instance, the attack pattern of a DDoS attack can be studied and used to prevent it in the future. Similarly, social media sites can identify fake profiles, information spread patterns, and more to detect any anomaly and prevent site-level attacks. Sites can protect themselves from suspicious traffic by analyzing incoming data and traffic patterns.

Benefits of pattern recognition technology

Pattern recognition is an important basis of many AI-based image recognition and forecasting tools. It can be effectively used in various security and educational applications. Some of the advantages of pattern recognition are listed below:

  • It can be used in several applications where a classification task must be based on similar characteristics. Notable applications include medical imaging and diagnosis, where tumors can be classified into cancerous and non-cancerous by pattern recognition.
  • It can be an excellent method to identify objects within an image, especially effective in discovering hidden and distant objects that may appear different from different angles.
  • Pattern recognition results can be further extrapolated to make predictive results, such as forecasting market conditions based on historical data.
  • Pattern recognition can help uncover unknown data.
  • It can also be used to make useful suggestions and conclusions based on recognized patterns.

Challenges and concerns of pattern recognition technology

  • For more accurate pattern recognition, more data is required. This makes it a data-intensive technique where you need huge amounts of data to achieve higher accuracy. This can thus lead to storage issues and challenges regarding the performance of the applications using pattern recognition.
  • The accuracy and correctness of pattern recognition also depend on the data quality. Since it also requires huge amounts of data, gathering quality data from reliable sources can present a challenge.
  • Pattern recognition that uses supervised learning will also require longer training time, given the huge amounts of data it deals with.

Future of pattern recognition technology

Pattern recognition is a vast field with a large scope for development and further research. The possibilities of future applications based on pattern recognition are endless. It can be coupled with various industries to create intelligent systems that are more personalized and fault tolerant. Pattern recognition can be expected to play a big role in further developing computer vision systems where a large amount of image data is dealt with. Medical imaging is a field where pattern recognition has found a great boost and is increasingly used to develop advanced diagnostic systems using images and other relevant medical data.

Similarly, it can be used in natural language processing systems to further improve chatbots’ intelligence, automatic text generation, and more. It also finds great use in search engines and social media analysis where user behavior and trends are analyzed to identify and predict user behavior patterns.

Strategic gameplaying can also further improve their gameplay by properly using pattern recognition models to provide users with more personalized gameplay.

Final Words

Pattern recognition is a big part of today’s AI and machine learning-based applications. This field is set to grow with more advanced learning models in development and is considered one of the most rapidly advancing fields within machine learning. Pattern recognition techniques are used in almost every ML-based application, from chatbots to search engines, making it a highly sought-after skill for aspiring data scientists

Pattern Recognition FAQ

What is pattern recognition?

Pattern recognition is a branch of artificial intelligence that focuses on the detection and identification of patterns and regularities in data. It involves the classification or categorization of data based on the information fetched from different patterns and similarities in the data. This could be in various forms, such as visual patterns, speech patterns, or patterns in data streams.

What are the types of pattern recognition?

There are primarily two types of pattern recognition: supervised and unsupervised. In supervised pattern recognition, the model is trained on a labeled dataset to understand and recognize the pattern. In contrast, unsupervised pattern recognition does not use any predefined labels, leaving the model to infer patterns and structures directly from the input data.

What are some practical applications of pattern recognition?

Pattern recognition has a wide range of applications in numerous fields. In image processing, it's used for facial recognition, handwriting recognition, and object detection. In healthcare, it can help diagnose diseases based on symptoms or imaging data. In finance, it's applied in credit scoring and fraud detection. Other applications include speech recognition in communication systems, data mining in information technology, and quality control in manufacturing.

How does pattern recognition differ from machine learning?

Pattern recognition and machine learning often overlap and are used interchangeably. However, there is a subtle difference. Pattern recognition primarily focuses on making sense of specific patterns in the data and assigning some meaning to these patterns. Machine learning, on the other hand, focuses on improving the performance of a machine or system in making predictions or decisions based on the learning from data.

What are some challenges in pattern recognition?

Some of the challenges in pattern recognition include dealing with high dimensional data where traditional methods may fail due to the 'curse of dimensionality.' Noise and distortion in data can also create significant challenges in recognizing patterns accurately. Furthermore, the need for large amounts of labeled data for supervised pattern recognition can also be a limiting factor. Lastly, ensuring the model's robustness to variations in patterns while avoiding overfitting is a significant challenge.