Let’s recall together how many times have we turned to writing assistants to check this or that grammar structure, spelling, or punctuation? With over 7 million daily active and 30 million accumulative users, Grammarly has massively impacted the writing culture, whether academic, professional communication, or casual messaging. Little do we know our go-to AI-powered savior performs so well because of its ability to spot patterns. Take comma; the model had to learn the patterns of proper comma usage to effectively identify misused punctuation. Now, imagine the actual amount of pattern recognition applications in our surroundings that we’re not fully aware of. At the end of the day, how do we progress from a rough idea of pattern recognition to an actual working mechanism? We’ll cover that in a minute.
In this article, we’ll introduce the basics of pattern recognition with the following breakdown:
- What is pattern recognition?
- Understanding pattern recognition in machine learning
- Types of pattern recognition algorithms
- Where can pattern recognition be implemented?
- Wrapping up
What is pattern recognition?
We, as humans, are evolutionarily wired to recognize patterns and match them to our stored memories. In its broader definition, pattern recognition is one’s ability to memorize and retrieve patterns upon constant exposure to its repetition. In machine learning, pattern recognition is referred to matching the database information with the incoming data. In other words, models rely on what they’ve been introduced to effectively identify commonalities.
Despite subtle intersections such as image classification for pattern recognition, computer vision and pattern recognition are different by and large. Pattern recognition treats various data sorts and relates to automated pattern discovery while computer vision focuses on image processing, object detection, image classification, and segmentation, without utterly relying on pattern recognition.
Understanding pattern recognition in machine learning
As one of the building blocks of computer vision, pattern recognition aims to imitate the capabilities of the human brain. Think about it this way: predictions on unseen data are feasible because of a models’ ability to identify recurring patterns. In the meantime, that may happen with any data format, be it an image, video, text, audio, etc.
While inherently complex, pattern recognition involves analyzing the input data, extracting patterns, and comparing them against the stored data. The procedure can be broken down into two phases: explorative, when the algorithms explore patterns, and descriptive, when algorithms group and attribute the found patterns to the initial data. If we break this further, pattern recognition in machine learning encompasses the following path:
Thoroughly designed high-quality ground-truth datasets are a must to achieve the desired level of accuracy in recognition. Here, using open-source datasets may cut off a great deal of time, as opposed to the tedious manual data collection. Nonetheless, data quality control should still be your priority. An alternative scenario is when your data is impossible to collect manually and the only way to go is to generate or design artificial sets on your own, i.e., synthetic datasets.
Pre-processing is all about fixing impurities to produce more comprehensive sets of data and increase the chances of top-notch predictions. Smoothing and normalization to correct the image from strong variations in lighting direction and intensity are also key considerations for this step. This way, you’ll create meaningful and easily interpretable data for models.
At this stage, the input data is transformed into a feature vector, a reduced representation of a set of features. That is to solve the issue of the high-dimensionality of the input set, which means that only relevant information, namely selected features should be extracted, as opposed to a full-size input. You have to make sure the features are insensitive towards distortions or manipulation of any kind. Out of these features, you should select the inputs with the highest potential in results accuracy. Once all is done, these features are sent for classification.
Extracted features are used to compare them against similar patterns, associating each one to the relevant class. The learning procedure, as we know, can take place in two ways: With supervised learning, the classifiers will have prior knowledge of each pattern category on top of the metrics and relevant parameters to distinguish among different patterns. With regard to unsupervised learning, the parameters are defined or updated upon the introduction of the input data. The model here relies on the inherent patterns in data it is capable of determining to generate the desired output. Final heads-up: pattern recognition doesn’t end with the raw output. It is usually followed by post-processing, which involves further decision-making on how to use those results to properly guide the system.
Types of pattern recognition algorithms
One of the more challenging parts of pattern recognition is deciding on the algorithms you’re planning to stick with. We’ll briefly mention six common algorithms in recognition:
The methodology itself is massive. The outputs are reliant on probability, yet by and large, it uses statistical techniques to learn from examples. This way the model gathers observations to study and come up with working rules that are can potentially be implemented for future observations.
The statistical method is not best suited for complex pattern recognition. This is where structural recognition comes in with its hierarchical approach and categorization into subclasses. The model describes complex relationships between multiple elements and serves purposes such as image and shape analyses, where measurable structures are confirmed.
As expected, this method utilizes artificial neural networks and is more flexible in comparison with traditional algorithms. Neural networks are efficient in classification, deploying biological concepts to recognize patterns. When it comes to pattern recognition, the most effective method is feed-forward networks, where learning takes place by giving feedback to the input patterns.
Template matching is used when dealing with two entities of the same type. Here, the target pattern is matched with a stored template, where the similarity is determined between entities such as curves, shapes, etc. The method, however, requires an excessive number of templates and is rather rigid when measured up against the existing alternatives.
In real-world recognition problems, fuzziness (many-valued logic, where the truth value of variables can be any real number between 0 and 1) is pervasive, which is greatly attributed to our cognitive system. More often than less, we are faced with uncertain components when scanning objects for recognition through our visual system. That holds just as true in a digital world, which explains the vast applicability of the algorithm.
A hybrid model typically describes a combination of different types of algorithms to deploy the advantages of all the methods used. It recognizes patterns through multiple classifiers, where each is trained based on feature spaces. A conclusion is drawn based on the accumulation of classifier sets, whose accuracy is detected through a decision function.
Where can pattern recognition be implemented?
When there’s a similar variety of algorithms out there, the bar for what’s expected to be on the pattern recognition applications list lifts up automatically. Still, examples are limitless. Below we’ll mention several areas incorporating pattern recognition one way or another.
- NLP: Recognition algorithms help draw insights based on the patterns in data for applications such as plagiarism detection, text generation, translation, grammar correction, etc.
- Fingerprint scanning: We stumble across biometric scanning within arm’s reach. Modern smartphones and laptops have a fingerprint identification feature that provides an added layer of protection. That is happening all because the device has learned the features of your fingerprint through pattern analysis.
- Seismic activity analysis: This one is all about observing how earthquakes and analogous natural events affect the earth’s crust: soil, rocks, and buildings. By using recurring patterns in seismic records, scientists can build disaster resilience models to mitigate the effects of seismic activity on time.
- Audio and voice recognition: Speech-to-text converters and personal assistants are all examples of audio and voice recognition systems operating based on pattern recognition. Let’s not go too far—Siri, Alexa, Shazam—these titans perceive and analyze audio and voice signals to derive meaning by encoding words and phrases.
- Computer vision: Pattern recognition has various applications in computer vision, ranging from biological to medical imaging. It can be applied in damaged leaf, infected cell detection and much more.
Rapid advances in pattern recognition algorithms continue to offer more intuitive solutions to real-world problems. Today, the recognition system has the potential to evolve into a more agile process that continuously underpins the development of AI. We hope this article provides you with more context and insight into the difference between pattern recognition, machine learning, and computer vision, how machines recognize patterns, along with an overview of relevant algorithms. Odds are you’ll need help with training data for a recognition model. At SuperAnnotate, we are committed to helping companies build super high-quality data up to 5x faster. Want to be the next?