Introduction to Pattern Recognition Algorithms
Pattern Recognition has been attracting the attention of scientists across the world. In the last decade, it has been widespread among various applications in medicine, communication systems, military, bioinformatics, businesses, etc. Pattern recognition can be defined as the recognition of surrounding objects artificially. Naturally, the process of recognition is complex task artificially. This is accomplished in machines via machine learning and pattern recognition specific algorithms. Pattern Recognition gives the solution to problems like facial expressions recognition, speech recognition, classification, healthcare, GIS, remote sensing, image analysis, etc.
The performance of the PR methods is affected by these components.
- Data and its amount
- The technology or method used.
- The Designer of the algorithms and its user
Algorithms in Pattern Recognition
The toughest part of PR systems is to choose the appropriate model. In this article, we will discuss the algorithms related to pattern recognition technique.
PR algorithms can be categorized into six types based on a survey.
- Statistical Techniques
- Structural Techniques
- Template Matching
- Neural Network Approach
- Fuzzy Model
- Hybrid Models
1. Statistical Algorithm Model
In this model, the pattern is termed in the form of features. These Features are selected in a way that different patterns take space without overlapping. It is able to predict and recognize the probabilistic nature. It works so nicely that the selected features are helping the formation of clusters. It analyses the probability distribution, decision boundaries, etc., for the patterns. The machine learns and adapts accordingly. Then these patterns are projected to further processing, training. Then we apply testing patterns for recognition of patterns. This leads to further classification methods. The various schemes used in it are Baye’s Decision Rule, PCA, etc.
Fig. 1: Statistical Pattern Recognition Model
2. Structural Algorithm Model
When we see some patterns with strong structures, statistical models are a little difficult to be used for recognition. Thus we need a structural model in complex pattern recognition like multi-dimensional entities. Here patterns are hierarchical in nature categorized further into subclasses. In the structural approach, we follow a scheme of pattern recognition that is ruled by sub-patterns related to each other. The model is extended to structure and its forms in patterns. There is an increased power in the description of finite automata. The language is PR language which seems primitive, yet it is powerful in pattern recognition. The languages that are context-sensitive are presented by using procedures deterministic in nature. The selection type of grammar for PR systems relies on grammar and primitive rules. This model is successfully used in the shape analysis, contours, image analysis where finite structures are guaranteed.
3. Template Matching Algorithm Model
The model of Template matching is simplest. It is most primitive of all the models. The model is used to determine similarity among two images. The pattern matched is being stored in templates, and the templates are given flexibility for scalar and rotational changes. The competence of this model relies upon the already stored templates in the database. We take correlation function to be the function of recognition in this case, and later it is optimized according to the availability of the training set. The only problem with this model is that this approach is not as efficient while working in distorted patterns.
4. Neural Network-Based Algorithm Model
Neural networks are the most widely used model. They are composed of parallel structures or subunits called neurons. They are efficiently used in Classification. The property of changing the weights repeatedly on iteration patterns, learning abilities, gives this model an edge of competence over other existing models. Perceptron is one of the oldest neuron models. It is basically a two-layered structure. If we find the output function as a step, then it does classification problems. If this is linear, then it is supposed to solve the regression problem.
The very commonly used is Feed-Forward Backpropagation neural networks, also acronym as FFBPNN. The variety of neural networks is used for different tasks in recognition of patterns and requirement function. The performance of the neural networks improves as the numbers increase for hidden layers. The number of neurons should also be large to be able to represent the problem and find the patterns hidden in them. Thus there is a requirement of the trade-off between size and complexity of the network.
5. Fuzzy Based Algorithm Model
The fuzzy algorithms are quite complex in nature yet produce the best pattern recognition results. This is because the modelling is for uncertain domains and components for recognition. This can be understood as a part of the probabilistic approach. Most real-world features are fuzzy in nature; therefore, we can apply the fuzzy model in almost maximum pattern recognition schemes. We use a syntactic approach for the patterns related to formal languages. Semantic techniques can be said to be used when there is a requirement of fuzzy partitions of data sets. Later we find the similarity index depending on the weights of the distance between fuzzy sets and reference sets.
6. Hybrid Algorithm Model
Now that we know a few approaches to pattern recognition algorithms, we can say that there is no single algorithm completely efficient in all cases. SO we need to deploy multiple algorithms together. This leads to the birth of a new algorithm called a hybrid model for PR algorithms. We are no bale to decide the best classifier until the prior knowledge is available. To enhance the performance of the system, we can use a set of classifiers and combiners for drawing the final conclusion. The usage of multiple classifiers enhances the performance of the system. Every individual classifier is trained in different feature spaces. A decision function is designed to decide the classifiers and their accuracy. The optimization is implemented to obtain the decision to form a set of classifiers.
We have enlisted the comparative view of different algorithms of Pattern recognition. A wise decision is to utilize these algorithms according to the need of the problem statements. To recognize unknown shapes, we use the fuzzy method. This improves the strength of Pattern recognition algorithms and adds a variety in a hybrid approach.
This is a guide to Pattern Recognition Algorithms. Here we discuss the introduction to Pattern Recognition Algorithms along with the 6 different algorithms explained in detail. You can also go through our other related articles to learn more –