Pattern is everything around in this digital world. A pattern can either be seen physically or it can be observed mathematically by applying algorithms. Example: The colours on the clothes, speech pattern etc. In computer science, a pattern is represented using vector features values.
What is Pattern Recognition ?
Pattern recognition is the process of recognizing patterns by using machine learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation. One of the important aspects of the pattern recognition is its application potential. Examples: Speech recognition, speaker identification, multimedia document recognition (MDR), automatic medical diagnosis.
In a typical pattern recognition application, the raw data is processed and converted into a form that is amenable for a machine to use. Pattern recognition involves classification and cluster of patterns.
In classification, an appropriate class label is assigned to a pattern based on an abstraction that is generated using a set of training patterns or domain knowledge. Classification is used in supervised learning.
Clustering generated a partition of the data which helps decision making, the specific decision making activity of interest to us. Clustering is used in an unsupervised learning.
Features may be represented as continuous, discrete or discrete binary variables. A feature is a function of one or more measurements, computed so that it quantifies some significant characteristics of the object. Example: consider our face then eyes, ears, nose etc are features of the face.
A set of features that are taken together, forms the features vector. Example: In the above example of face, if all the features (eyes, ears, nose etc) taken together then the sequence is feature vector([eyes, ears, nose]). Feature vector is the sequence of a features represented as a d-dimensional column vector. In case of speech, MFCC (Melfrequency Cepstral Coefficent) is the spectral features of the speech. Sequence of first 13 features forms a feature vector.
Pattern recognition possesses the following features:
Pattern recognition system should recognize familiar pattern quickly and accurate
Recognize and classify unfamiliar objects
Accurately recognize shapes and objects from different angles
Identify patterns and objects even when partly hidden
Recognize patterns quickly with ease, and with automaticity.