Pattern Recognition

Pattern Recognition

Ever wonder how a robot can identify an object? The answer would be connected to the concept of pattern recognition. Pattern recognition is a scientific discipline whose aim is the classification of the objects into a lot of categories or classes. Pattern recognition is also a integral part in most machine intelligence system built for decision making. (Sergios Theodoridis, 2003). In other words, PR is the act of taking in raw data and making an action based on the “category” of the pattern via mathematical model. Pattern recognition system is consist of three associative functions which are data building, pattern analysis, and pattern classification. Data building acquire information from an object and convert into vector that can be processed by machine intelligence system. Subsequently, the pattern analysis function will process the data (vector) through feature selection, feature extraction, data-dimension compress, and some other possible feature. Next, the information acquired from pattern analysis is utilized to finalize categorization of the object. After these three steps, the data continues to the step of classification/ regression/ description showed in figure 1 which is the essential part of pattern recognition system. Classification is a PR problem of assigning an object to a class. The output of the classification PR system is an integer label, for example: classifying a product as “1” or “0” in a quality control test. Regression is a generalization of a classification task, and the output of the regression PR system is a real-valued number, such as predicting the share value of a firm based on past performance and stock market indicators. Description is the problem of representing an object in terms of a series of primitives, and the PR system produces a structural or linguistic description.

A general composition of a PR system is given below.

Figure 1. Composition of Pattern Recognition System

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