Basic Pattern Recognition Concept

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1 Concepts of Pattern Basic Pattern Concept Xiaojun Qi Pattern: A pattern is the description of an object. According to the nature of the patterns to be recognized, we may divide our acts of recognition into two major types: The recognition of concrete items The recognition of abstract items When a person perceives a pattern, he makes an inductive inference and associates this perception with some general concepts or clues which he has derived from his past experience. Thus, the problem of pattern recognition may be regarded as one of discriminating of the input data, not between individual patterns but between populations, via the search for features or invariant attributes among members of a population. The study of pattern recognition problems may be logically divided into two major categories: The study of the pattern recognition capability of human beings and other living organisms. (Psychology, Physiology, and Biology) The development of theory and techniques for the design of devices capable of performing a given recognition task for a specific application. (Engineering, Computer, and Information Science) Task of Classification Input Data Output Response Pattern recognition can be defined as the categorization of input data into identifiable classes via the extraction of significant features or attributes of the data from a background of irrelevant detail. Character Speech Speaker Weather Prediction Optical signals or strokes Acoustic waveforms Voice Weather maps Name of character Name of word Name of speaker Weather forecast Medical Diagnosis Symptoms Disease Stock Market Prediction Financial news and charts Predicted market ups and downs.

2 Pattern Class: It is a category determined by some given common attributes. Pattern: It is the description of any member of a category representing a pattern class. When a set of patterns falling into disjoint classes is available, it is desired to categorize these patterns into their respective classes through the use of some automatic device. The basic functions of a pattern recognition system are to detect and extract common features from the patterns describing the objects that belong to the same pattern class, and to recognize this pattern in any new environment and classify it as a member of one of the pattern classes under consideration. Fundamental Problems in Pattern System Design The first one is concerned with the representation of input data which can be measured from the objects to be recognized. The pattern vectors contain all the measured information available about the patterns. The measurements performed on the objects of a pattern class may be regarded as a coding process which consists of assigning to each pattern characteristic a symbol from the alphabet set. When the measurements yield information in the form of real numbers, it is often useful to think of a pattern vector as a point in an n-dimensional Euclidean space. The set of patterns belonging to the same class corresponds to an ensemble of points scattered within some region of the measurement space. The second problem concerns the extraction of characteristic features or attributes from the received input data and the reduction of the dimensionality of pattern vectors. (This is often referred to as the preprocessing and feature extraction problem.) The features of a pattern class are the characterizing attributes common to all patterns belonging to that class. Such features are often referred to as intraset features. The features which represent the differences between pattern classes may be referred to as the interset features. The elements of intraset features which are common to all pattern classes under consideration carry no discriminatory information and can be ignored. The extraction of features has been recognized as an important problem in the design of pattern recognition systems. The third problem involves the determination of optimum decision procedures, which are needed in the identification and classification process. If completed a prior knowledge about the patterns to be recognized is available, the decision functions may be determined with precision on the basis of this information. If only qualitative knowledge about the patterns is available, reasonable guesses of the forms of the decision functions can be made. Need adjustment as necessary. If there exists little, if any, a priori knowledge about the patterns to be recognized, a training or learning procedure is needed. The patterns to be recognized and classified by an automatic pattern recognition system must possess a set of measurable characteristics. Correct recognition will depend on The amount of discriminating information contained in the measurements; The effective utilization of this information. Design Concepts and Methodologies Membership-roster Concept Characterization of a pattern class by a roster of its members suggests automatic pattern recognition by template matching. The membership-roster approach will work satisfactorily under the condition of nearly perfect pattern samples.

3 Common-property Concept Characterization of a pattern class by common properties shared by all of its members suggests automatic pattern recognition via the detection and processing of similar features. The basic assumption in this method is that the patterns belonging to the same class possess certain common properties or attributes which reflect similarities among these patterns. Advantage: (Membership-roster Concept vs. Common-property Concept) The storage requirement for the features of a pattern class is much less severe than that for all the patterns in the class. Significant pattern variations cannot be tolerated in template matching. If all the features of a class can be determined from sample patterns, the recognition process reduces simply to feature matching. Clustering Concept When the patterns of a class are vectors whose components are real numbers, a pattern class can be characterized by its clustering properties in the pattern space. If the classes are characterized by clusters which are far apart, simple recognition schemes such as the minimum-distance classifiers may be successfully employed. When the clusters overlap, it becomes necessary to utilize more sophisticated techniques for partitioning the pattern space. Overlapping clusters are the result of: A deficiency in observed information; The presence of measurement noise. The degree of overlapping can often be minimized by: Increasing the number and the quality of measurements performed on the patterns of a class. The basic design concepts for automatic pattern recognition described above may be implemented by three principal categories of methodology: Heuristic; Mathematical; Linguistic or syntactic. Heuristic Methods: The heuristic approach is based on human intuition and experience, making use of the membership-roster and common-property concepts. A system designed using this principle generally consists of a set of ad hoc procedures developed for specialized recognition tasks. Decision is based on ad hoc rules. Example: Character recognition (Detection of features such as the number and sequence of particular strokes)

4 Mathematical Methods: It is based on classification rules which are formulated and derived in a mathematical framework, making use of the common-property and clustering concepts. Deterministic approach: Does not employ explicitly the statistical properties of the pattern classes. Statistical approach: It is formulated and derived in a statistical framework. Example: Bayes classification rule and its variations. This rule yields an optimum classifier when the probability density function of each pattern population and the probability of occurrence of each pattern class are known. Linguistic (Syntactic) Methods: Characterization of patterns by primitive elements (subpatterns) and their relationships suggests automatic pattern recognition by the linguistic or syntactic approach, making use of the common-property concept. A pattern can be described by a hierarchical structure of subpatterns analogous to the syntactic structure of languages. This permits application of formal language theory to the pattern recognition problem. This approach is particularly useful in dealing with patterns which cannot be conveniently described by numerical measurements or are so complex that local features cannot be identified and global properties must be used. In a supervised learning environment, the system is taught to recognize patterns by means of various adaptive schemes. The essentials of this approach are a set of training patterns of known classification and the implementation of an appropriate learning procedure. The unsupervised pattern recognition techniques are applicable to the situations where only a set of training patterns of unknown classification may be available. Examples of Automatic Pattern Systems Character : Technique Used: Rather than being compared with pre-stored patterns, handprinted characters are analyzed as combinations of common features, such as curved lines, vertical and horizontal lines, corners, and intersections. Automatic Classification of Remotely Sensed Data: Examples: Land use, crop inventory, cropdisease detection, forestry, monitoring of air and water quality, geological and geographical studies, and weather prediction, plus a score of other applications of environmental significance. Technique Used: Bayes classifier Biomedical Applications: Technique Used: Pattern primitives, such as long arcs, short arcs, and semi-straight segments, which characterize the chromosome boundaries are defined. When combined, these primitives form a string or symbol sentence which can be associated with a so-called pattern grammar. There is one grammar for each type (class) of chromosome.

5 Fingerprint : Technique Used: It detects tentative minutiae and records their precise locations and angles. Nuclear Reactor Component Surveillance: Technique Used: Detect the clusters of pattern vectors by iterative applications of a cluster-seeking algorithm. The data cluster centers and associated descriptive parameters, such as cluster variances, can then be used as templates against which measurements are compared at any given time in order to determine the status of the plants. Significant deviations from the pre-established characteristic normal behavior are flagged as indications of an abnormal operating conditions. A Simple Pattern Model A simple scheme for pattern recognition consists of two basic components: Sensor: It is a device which converts a physical sample to be recognized into a set of quantities which characterize the sample. Categorizer: It is a device which assigns each of its admissible inputs to one of a finite number of classes or categories by computing a set of decision functions. We assume that the a priori probabilities for the occurrence of each class are equal, that is, it is just as likely that x comes from one class as from another.

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