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What Is Pattern Recognition?

Pattern recognition is an automated process thanks to the availability of computer power to ingest data, process it, recognize its patterns and share it for further analysis. Here’s how pattern recognition works.

Pattern recognition is a process for automating the identification and exploration of patterns in data sets . Since there’s no single way to recognize data patterns, pattern recognition ultimately depends on:

• The ultimate goal of any given pattern recognition workflow
• The type of data available (quantitative vs. qualitative, time series data vs. point-in-time data)
• The computing power and storage available to process and manage the data

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How Pattern Recognition Works

Pattern recognition is a process made of the same steps that anyone concerned with finding patterns in data goes through.

Pattern Recognition Process

• Define the problem
• Be aware of the null hypothesis
• Choose a methodology
• Measure uncertainty
• Test and iterate over the results

1. Define the Problem

Defining the problem is always the first step in any pattern recognition project. This is where you formulate research questions or hypotheses regarding the data and its patterns. For example, you may be concerned with capturing holiday and seasonal effects (patterns) in shopping data coming from shopping malls’ databases . A specific question we may want to ask about this data is whether shoppers tend to display sensitive responses to specific promotions or discounts the company launches through email marketing campaigns and whether these tend to distribute in any particular way throughout the year.

2. Be Aware of the Null Hypothesis 

In the field of statistics and hypothesis testing, searching to prove the existence of a relationship between variables and finding none is called accepting the null hypothesis. Not all data may have patterns hidden within it. Moving into the analysis, it’s important to remember that the process of pattern recognition may also not yield results. That is to say, you may be looking for patterns where there simply are none.

3. Choose a Methodology

There are many different ways to find patterns and it’s important to evaluate all potential models that may apply to the problem at hand. After all, there may be more than one.

4. Measure Uncertainty 

Models used to find data patterns are as accurate as they can be within an uncertain world. It’s important to treat pattern recognition under a probabilistic lens to factor in uncertainty, especially when pattern recognition is put to use for predictive purposes. 

5. Test and Iterate Over the Results

Constant iteration over pattern recognition processes is necessary to ensure optimal results and avoid losing relevance or accuracy as time passes. Once you’ve landed on a problem and model, and measured patterns, it’s important to remember that the workflow does not stop there. 

Keep testing pattern recognition methods to make sure they accurately capture trends in the underlying data even as time and conditions go on.

Features of Pattern Recognition 

Pattern recognition has several applications, but there are a few key tenets that are common regardless of the domain.

Statistical Approach

Pattern recognition is rooted in statistics . When we’re finding patterns in data, we always need to account for variability, uncertainty and the probable distributions, if any, that data holds.

The field of statistics is also the precursor to modern pattern recognition approaches. As a result, a statistical lens is appropriate for most, if not all, modern pattern recognition applications.

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Algorithmic Nature

An algorithm is a procedure that follows a precise sequence of steps. Depending on the nature of the problem and the kind of data at hand, you can use many different algorithms.

The main groups of algorithms used for pattern recognition include:

• Classification Algorithms
• Ensemble Learning
• Regression Algorithms
• Sequence Labeling Methods

Data Categorization

While you’re defining a pattern recognition project’s problem, your main concern is usually fitting the data into specific categories, or labels, that are linked to the underlying patterns the data holds.

For example, in time series data analysis , you may be most concerned with understanding the seasonal component of monthly sales data, a category specific to the seasonal pattern you see in the data. You might see sales spikes during the Christmas holiday season.

Reliance on Abundant Data and Processing Power

Pattern recognition has become increasingly prevalent since the technological advances in computing started around the turn of the 21st century. With these advances we can:

• process more data
• process data faster (given equal data size) thanks to making use of grid computing , which is the use of many different computers to distribute the computational load across a higher number of servers
• store data less expensively thanks to the rise of modern cloud database management solutions

Advantages of Pattern Recognition

High automation potential.

Pattern recognition workflows have the benefit of being a great fit for full end-to-end automation. This means we can configure, program and structure pattern recognition workflows to run with minimal human intervention, once we’ve completed the initial setup and analysis.

In other words, teams developing pattern recognition solutions can benefit from a low-touch, high-return analytical workflow. 

Efficiency 

Automation also brings an additional advantage, which is letting subject-matter experts focus on the least intuitive and most complex parts of pattern recognition problems. This is resource-efficient because it brings down the cost of labor and overall time dedicated to developing solutions.

Most organizations can also benefit from plug-and-play situations wherein they simply translate similar pattern recognition problems to their domain with minimal effort. Examples of this include re-using code and/or algorithms already developed by others, especially if they’re available from open-source projects.

Applications for Descriptive and Predictive Analytics 

Pattern recognition is incredibly flexible because it can be used to extract trends from historical data and diagnose what happened in the past (descriptive pattern recognition). We can also use pattern recognition methodologies to make inferences about the future (predictive pattern recognition).

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Examples of Pattern Recognition

Cybersecurity and voice detection.

A cybersecurity company selling digital security services to client firms can use pattern recognition to develop software that automatically recognizes who is speaking from audio files coming from employee phone calls. We can then use this technology for any number of applications where there may be a use case for monitoring professional phone calls for security or training purposes.

Healthcare Technology and Medical Diagnosis

A medical institution is concerned with helping doctors in identifying early-stage cancer development. Using pattern recognition and a set of digital images , the organization can detect early-stage cancer with high probability, thereby helping patients receive earlier treatment with a higher probability of success.

Marketing and Customer Churn Prevention

A grocery store chain is interested in monitoring its base of loyalty card customers for early indications of customer attrition. The company is interested in this information so it can react promptly by offering incentives and additional offers to these customers to stop them from churning .

We can also put pattern recognition algorithms to good use on the chain’s customer data set to cluster them into different levels of churn probability and identify the churn prevention initiative’s target customers.

Applications of Pattern Recognition

Computer vision.

Pattern recognition methodologies are incredibly popular in computer vision . We can put pattern recognition methodologies to use to programmatically develop applications that derive knowledge from images, and effectively understand them as a human being might.

Machine Learning

Machine learning , a subset of data science , makes use of computing power to derive insights from data using specific learning algorithms. This is one of the most prevalent current applications of pattern recognition and is at the heart of the advancements in AI development in most industries. 

Time Series Analysis

Time series data is essentially logs of data over time. Historical stock prices are an example of time series data. You might also think about sensor and telemetry data from video cameras.

Pattern recognition is key to understanding, analyzing, and even forecasting time series data . This is because time series data is filled with different components (or patterns) that are useful to extract and understand to make sense of the data.

Examples of these time series data components are seasonal effects (such as the ones determined by the Black Friday shopping season for example) and cyclical effects (longer-term trends, such as the steady growth in the value of the stock market).

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What is Pattern Recognition?

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Pattern Recognition | Basics and Design Principles

Prerequisite – Pattern Recognition | Introduction Pattern Recognition System Pattern is everything around in this digital world. A pattern can either be seen physically or it can be observed mathematically by applying algorithms. In Pattern Recognition , pattern is comprises of the following two fundamental things:

• Collection of observations
• Differentiate between good and bad features.
• In a statistical-classification problem, a decision boundary is a hypersurface that partitions the underlying vector space into two sets. A decision boundary is the region of a problem space in which the output label of a classifier is ambiguous. Classifier is a hypothesis or discrete-valued function that is used to assign (categorical) class labels to particular data points.

• A Sensor : A sensor is a device used to measure a property, such as pressure, position, temperature, or acceleration, and respond with feedback.
• A Preprocessing Mechanism : Segmentation is used and it is the process of partitioning a data into multiple segments. It can also be defined as the technique of dividing or partitioning an data into parts called segments.
• A Feature Extraction Mechanism : feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations. It can be manual or automated.
• A Description Algorithm : Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform “most likely” matching of the inputs, taking into account their statistical variation
• A Training Set : Training data is a certain percentage of an overall dataset along with testing set. As a rule, the better the training data, the better the algorithm or classifier performs.
• Statistical Approach and
• Structural Approach
• Descriptive Statistics: It summarizes data from a sample using indexes such as the mean or standard deviation.
• Inferential Statistics: It draw conclusions from data that are subject to random variation.
• Sentence Patterns
• Phrase Patterns

Pattern recognition is a subfield of machine learning that focuses on the automatic discovery of patterns and regularities in data. It involves developing algorithms and models that can identify patterns in data and make predictions or decisions based on those patterns.

There are several basic principles and design considerations that are important in pattern recognition:

• Feature representation: The way in which the data is represented or encoded is critical for the success of a pattern recognition system. It is important to choose features that are relevant to the problem at hand and that capture the underlying structure of the data.
• Similarity measure: A similarity measure is used to compare the similarity between two data points. Different similarity measures may be appropriate for different types of data and for different problems.
• Model selection: There are many different types of models that can be used for pattern recognition, including linear models, nonlinear models, and probabilistic models. It is important to choose a model that is appropriate for the data and the problem at hand.
• Evaluation: It is important to evaluate the performance of a pattern recognition system using appropriate metrics and datasets. This allows us to compare the performance of different algorithms and models and to choose the best one for the problem at hand.
• Preprocessing: Preprocessing is the process of preparing the data for analysis. This may involve cleaning the data, scaling the data, or transforming the data in some way to make it more suitable for analysis.
• Feature selection: Feature selection is the process of selecting a subset of the most relevant features from the data. This can help to improve the performance of the pattern recognition system and to reduce the complexity of the model.

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Pattern Recognition

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Pattern Recognition. Speaker: Wen-Fu Wang Advisor: Jian-Jiun Ding E-mail: [email protected] Graduate Institute of Communication Engineering National Taiwan University, Taipei, Taiwan, ROC. Outline. Introduction Minimum Distance Classifier Matching by Correlation

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Pattern Recognition • Speaker: Wen-Fu Wang • Advisor: Jian-Jiun Ding • E-mail: [email protected] • Graduate Institute of Communication Engineering • National Taiwan University, Taipei, Taiwan, ROC

Outline • Introduction • Minimum Distance Classifier • Matching by Correlation • Optimum statistical classifiers • Matching Shape Numbers • String Matching

Outline • Syntactic Recognition of Strings String Grammars • Syntactic recognition of Tree Grammars • Conclusions

Sensor Feature generation Feature selection Classifier design System evaluation Introduction • Basic pattern recognition flowchart

Introduction • The approaches to pattern recognition developed are divided into two principal areas: decision-theoretic and structural • The first category deals with patterns described using quantitative descriptors, such as length, area, and texture • The second category deals with patterns best described by qualitative descriptors, such as the relational descriptors.

Minimum Distance Classifier • Suppose that we define the prototype of each pattern class to be the mean vector of the patterns of that class: • Using the Euclidean distance to determine closeness reduces the problem to computing the distance measures j=1,2,…,W(1) j=1,2,…,W (2)

Minimum Distance Classifier • The smallest distance is equivalent to evaluating the functions • The decision boundary between classes and for a minimum distance classifier is j=1,2,…,W(3) j=1,2,…,W (4)

Minimum Distance Classifier • Decision boundary of minimum distance classifier

Minimum Distance Classifier • Advantages: 1. Unusual direct-viewing 2. Can solve rotation the question 3. Intensity 4. Chooses the suitable characteristic, then solves mirror problem 5. We may choose the color are one kind of characteristic, the color question then solve.

Minimum Distance Classifier • Disadvantages: 1. It costs time for counting samples, but we must have a lot of samples for high accuracy, so it is more samples more accuracy! 2. Displacement 3. It is only two features, so that the accuracy is lower than other methods. 4. Scaling

Matching by Correlation • We consider it as the basis for finding matches of a sub-image of size within an image of size , where we assume that and for x=0,1,2,…,M-1,y=0,1,2,…,N-1(5)

Origin K J o M Matching by Correlation • Arrangement for obtaining the correlation of and at point

Matching by Correlation • The correlation function has the disadvantage of being sensitive to changes in the amplitude of and • For example, doubling all values of doubles the value of • An approach frequently used to overcome this difficulty is to perform matching via the correlation coefficient • The correlation coefficient is scaled in the range-1 to 1, independent of scale changes in the amplitude of and

Matching by Correlation • Advantages: 1.Fast 2.Convenient 3.Displacement • Disadvantages: 1.Scaling 2.Rotation 3.Shape similarity 4.Intensity 5.Mirror problem 6.Color can not recognition

Optimum statistical classifiers • The probability that a particular pattern x comes from class is denoted • If the pattern classifier decides that x came from when it actually came from , it incurs a loss, denoted

Optimum statistical classifiers • From basic probability theory, we know that

Optimum statistical classifiers • Thus the Bayes classifier assigns an unknown pattern x to class

Optimum statistical classifiers • The Bayes classifier then assigns a pattern x to class if, • or, equivalently, if

Optimum statistical classifiers • Bayes Classifier for Gaussian Pattern Classes • Let us consider a 1-D problem (n=1) involving two pattern classes (W=2) governed by Gaussian densities

Optimum statistical classifiers • In the n-dimensional case, the Gaussian density of the vectors in the jth pattern class has the form

Optimum statistical classifiers • Advantages: 1. The way always combine with other methods, then it got high accuracy • Disadvantages: 1.It costs time for counting samples 2.It has to combine other methods

1 2 3 1 0 4 0 2 7 5 6 3 Matching Shape Numbers • Direction numbers for 4-directional chain code, and 8-directional chain code

Matching Shape Numbers • Digital boundary with resampling grid superimposed

Order6 Order4 Chain code: 0321 Difference : 3333 Shape no. : 3333 Chain code: 003221 Difference : 303303 Shape no. : 033033 Order8 Chain code: 00332211 Difference : 30303030 Shape no. : 03030303 Chain code:03032211 Difference :33133030 Shape no. :03033133 Chain code: 00032221 Difference : 30033003 Shape no. : 00330033 Matching Shape Numbers • All shapes of order 4, 6,and 8

Matching Shape Numbers • Advantages: 1. Matching Shape Numbers suits the processing structure simple graph, specially becomes by the line combination 2. Can solve rotation the question 3. Matching Shape Numbers most emphatically to the graph outline, Shape similarity also may completely overcome 4. The Displacement question definitely may overcome, because of this method emphatically to the relative position but is not to the position

Matching Shape Numbers • Disadvantages : 1. It can not uses for a hollow structure 2. Scaling is a shortcoming which needs to change, perhaps coordinates the alternative means 3. Intensity 4. Mirror problem 5. The color is unable to recognize

String Matching • Suppose that two region boundaries, a and b, are coded into strings denoted and ,respectively • Let represent the number of matches between the two strings, where a match occurs in the kth position if

String Matching • A simple measure of similarity between and is the ratio • Hence R is infinite for a perfect match and 0 when none of the corresponding symbols in and match ( in this case)

b b b b b b String Matching • Simple staircase structure. • Coded structure.

String Matching • Advantages: 1.Matching Shape Numbers suits the processing structure simple graph, specially becomes by the line combination 2.Can solve rotation the question 3.Intensity 4.Mirror problem 5.Matching Shape Numbers most emphatically to the graph outline, Shape similarity also may completely overcome 6. The Displacement question definitely may overcome, because of this method emphatically to the relative position but is not to the position

String Matching • Disadvantages: 1.It can not uses for a hollow structure 2.Scaling 3.The color is unable to recognize

Syntactic Recognition of Strings String Grammars • When dealing with strings, we define a grammar as the 4-tuple • is a finite set of variables called non-terminals, • is a finite set of constants called terminals, • is a set of rewriting rules called productions, • in is called the starting symbol.

Syntactic Recognition of Strings String Grammars • Object represented by its skeleton • primitives. • structure generated by using a regular string grammar b a c

Syntactic Recognition of Strings String Grammars • Advantages: 1.This method may use to a more complex structure 2.It is a good method for character set • Disadvantages: 1.Scaling 2.Rotation 3.The color is unable to recognize 4.Intensity 5.Mirror problem

Syntactic Recognition of Tree Grammars • A tree grammar is defined as the 5-tuple • and are sets of non-terminals and terminals, respectively • is the start symbol, which in general can be a tree • is a set of productions of the form , where and are trees • is a ranking function that denotes the number of direct descendants(offspring) of a node whose label is a terminal in the grammar

Syntactic Recognition of Tree Grammars • Of particular relevance to our discussion are expansive tree grammars having productions of the form • where are not terminals and k is a terminal

Syntactic Recognition of Tree Grammars • An object • Primitives used for representing the skeleton by means of a tree grammar c e b a d

Syntactic Recognition of Tree Grammars • For example c e b a d

Syntactic Recognition of Tree Grammars • Advantages: 1. This method may use to a more complex structure 2. It is a good method for character set 3. The Displacement question definitely may overcome, because of this method emphatically to the relative position but is not to the position

Syntactic Recognition of Tree Grammars • Disadvantages : 1. Scaling is a shortcoming which needs to change, perhaps coordinates the alternative means 2. Rotation 3. The color is unable to recognize 4. Intensity

Conclusions • The graph recognizes is covers the domain very widespread science, in the past dozens of years, all kinds of method is unceasingly excavated, also acts according to all kinds of probability statistical model and the practical application model but unceasingly improves. • The graph recognizes applies to each different application domain, actually often also simultaneously entrusts with the entire wrap to recognize the system different appearance, which methods thus we certainly are unable to define to are "best" the graph recognize the method.

Conclusions • Summary the seven approach to pattern recognition, each methods has advantages and disadvantages respectively. Therefore, we have to understand each method preciously. Then we choose the adaptable method for efficiency and accuracy. • The A method has obtained extremely good recognizing rate in some application and is unable to express the similar method applies mechanically in another application also can similarly obtain extremely good recognizing rate.

Conclusions • Below provides several possibilities solutions the method • 1. Scaling problem we may the reference area solve. • 2. Neural networks solves for rotation problem. • 3.The color question besides uses RBG to solve also may use the spectrum to recognize differently. • 4. Doing correlation with the reverse match filter for Intensity mirror problem • 5. We can use the measure of area for a hollow structure

References • [1] R. C. Gonzolez, R. E. Woods, "Digital Image Processing, Second Edition", Prentice Hall 2002 • [2] 蒙以正, "數位信號處理應用Matlab",旗標 2005 • [3] S. Theodoridis, K. koutroumbas, "Pattern Recognition", Academic Press 1999 • [4] W. K. Pratt ,"Digital Image Processing, Third Edition", John Wiley & Sons 2001 • [5] R. C. Gonzolez, R. E. Woods, S. L. Eddins, "Digital Image Processing Using MATLAB", Prentice Hall 2005 • [6] 繆紹綱, 數位影像處理 活用-Matlab, 全華2000 • [7] J. Schurmann, " A Unified View of Statistical and Neural Approaches" Pattern Classification, Chap4, John Wiley & Sons, Inc., 1996

References • [8]K. Fukunaga, “Introduction to Statistical Pattern Recognition”, Second Edition, Academic Press, Inc.,1990 • [9] E. Gose, R. Johnsonbaugh, and Steve Jost, "Pattern recognition and Image Analysis", Prentice Hall Inc., New Jersey, 1996 • [10] Robert J. Schalkoff, "Pattern Recognition: Statical, Structural and Neural Approaches", Chap5, John Wiley & Sons, Inc., 1992 • [11] J. S. Pan, F. R. Mclnnes, and M. A. Jack, "Fast Clustering Algorithm for Vector Quantization", Pattern Recognition 29, 511-518, 1996

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Call For Papers

This Call for Papers expects two kinds of contributions.

The track 1 on RR Frameworks is dedicated to the general topics of Reproducible Research in experimental Computer Science with clear links  to Image Processing and Pattern Recognition. Papers describing experiences, frameworks or platforms are welcome. The contributions might also include discussions on software libraries, experiences highlighting how the works benefit from Reproducible Research.

In the track 2 on RR Results , authors are invited to describe their works in terms of Reproducible Research. For example, authors of papers already accepted to ICPR might propose a companion paper describing the quality of the reproducible aspects. In particular the papers of this track can focus mainly (but not limited) for instance on:

• Algorithmic implementation details
• Influence of parameter(s) for the result quality (criteria to optimize them).
• Integration of source code in an other framework.
• Known limitations (or difficult cases).
• Future improvements.
• Installation procedure.

For this track, the topics could overlap with the main topics of the ICPR tracks:

• Geometry and Deep Learning
• Discrete Geometry and Mathematical Morphology
• Pattern Recognition and Machine Learning
• Computer Vision and Robot Vision
• Image, Speech, Signal, and Video Processing
• Document Analysis, Biometrics, and Pattern Recognition Applications.
• Biomedical Image Analysis and Applications