Concepts of Pattern Recognition • Pattern: A pattern is the description of an object. Basic Pattern Recognition • According to the nature of the patterns to Concept be recognized, we may divide our acts of recognition into two major types: Xiaojun Qi – The recognition of concrete items – The recognition of abstract items • The study of pattern recognition problems • When a person perceives a pattern, he may be logically divided into two major makes an inductive inference and categories: associates this perception with some general concepts or clues which he has – The study of the pattern recognition capability derived from his past experience. of human beings and other living organisms. (Psychology, Physiology, and Biology) • Thus, the problem of pattern recognition may be regarded as one of discriminating of the input data, not between individual – The development of theory and techniques for patterns but between populations, via the the design of devices capable of performing a search for features or invariant attributes given recognition task for a specific among members of a population. application. (Engineering, Computer, and Information Science) Task of Input Data Output Response Classification Character Optical signals or Name of Recognition strokes character • Pattern recognition can be defined as the categorization of input data into identifiable Speech Acoustic Name of word Recognition waveforms classes via the extraction of significant Speaker Voice Name of speaker features or attributes of the data from a Recognition background of irrelevant detail. Weather Weather maps Weather forecast Prediction Medical Symptoms Disease Diagnosis Stock Market Financial news Predicted market Prediction and charts ups and downs. Fundamental Problems in Pattern • Pattern Class: It is a category determined by some given common attributes. Recognition System Design • Pattern: It is the description of any member of a • The first one is concerned with the representation of category representing a pattern class. When a input data which can be measured from the objects to set of patterns falling into disjoint classes is be recognized. available, it is desired to categorize these – The pattern vectors contain all the measured information available about the patterns. The patterns into their respective classes through the measurements performed on the objects of a pattern use of some automatic device. class may be regarded as a coding process which • The basic functions of a pattern recognition consists of assigning to each pattern characteristic a system are to detect and extract common symbol from the alphabet set. features from the patterns describing the objects – When the measurements yield information in the that belong to the same pattern class, and to form of real numbers, it is often useful to think of a pattern vector as a point in an n-dimensional recognize this pattern in any new environment Euclidean space. and classify it as a member of one of the pattern – The set of patterns belonging to the same class classes under consideration. corresponds to an ensemble of points scattered within some region of the measurement space. • The second problem concerns the extraction of • The third problem involves the determination of characteristic features or attributes from the optimum decision procedures, which are needed received input data and the reduction of the dimensionality of pattern vectors. (This is often in the identification and classification process. referred to as the preprocessing and feature – If completed a prior knowledge about the patterns to extraction problem.) be recognized is available, the decision functions may – The features of a pattern class are the characterizing be determined with precision on the basis of this attributes common to all patterns belonging to that information. class. Such features are often referred to as intraset features. – If only qualitative knowledge about the patterns is – The features which represent the differences between available, reasonable guesses of the forms of the pattern classes may be referred to as the interset decision functions can be made. Need adjustment features. The elements of intraset features which are as necessary. common to all pattern classes under consideration carry no discriminatory information and can be ignored. – If there exists little, if any, a priori knowledge about – The extraction of features has been recognized as an the patterns to be recognized, a training or learning important problem in the design of pattern recognition procedure is needed. systems. Design Concepts and Methodologies • The patterns to be recognized and • Membership-roster Concept classified by an automatic pattern – Characterization of a pattern class by a roster recognition system must possess a set of of its members suggests automatic pattern measurable characteristics. recognition by template matching. • Correct recognition will depend on – The amount of discriminating information – The membership-roster approach will work contained in the measurements; satisfactorily under the condition of nearly – The effective utilization of this information. perfect pattern samples. • Common-property Concept • Advantage: (Membership-roster Concept – Characterization of a pattern class by common properties shared by all of its vs. Common-property Concept) members suggests automatic pattern – The storage requirement for the features of a recognition via the detection and processing pattern class is much less severe than that for of similar features. all the patterns in the class. – The basic assumption in this method is that – Significant pattern variations cannot be the patterns belonging to the same class tolerated in template matching. If all the possess certain common properties or features of a class can be determined from attributes which reflect similarities among sample patterns, the recognition process these patterns. reduces simply to feature matching. • Clustering Concept • Overlapping clusters are the result of: – When the patterns of a class are vectors – A deficiency in observed information; whose components are real numbers, a pattern class can be characterized by its – The presence of measurement noise. clustering properties in the pattern space. • The degree of overlapping can often be – If the classes are characterized by clusters minimized by: which are far apart, simple recognition schemes such as the minimum-distance – Increasing the number and the quality of classifiers may be successfully employed. measurements performed on the patterns of a class. – When the clusters overlap, it becomes necessary to utilize more sophisticated techniques for partitioning the pattern space. • Heuristic Methods: The heuristic approach is • The basic design concepts for automatic based on human intuition and experience, pattern recognition described above may making use of the membership-roster and be implemented by three principal common-property concepts. categories of methodology: – A system designed using this principle generally – Heuristic; consists of a set of ad hoc procedures developed – Mathematical; for specialized recognition tasks. – Linguistic or syntactic. – Decision is based on ad hoc rules. – Example: Character recognition (Detection of features such as the number and sequence of particular strokes) • Mathematical Methods: It is based on • Linguistic (Syntactic) Methods: Characterization classification rules which are formulated and of patterns by primitive elements (subpatterns) derived in a mathematical framework, and their relationships suggests automatic making use of the common-property and pattern recognition by the linguistic or syntactic clustering concepts. approach, making use of the common-property – Deterministic approach: concept. • Does not employ explicitly the statistical properties of – A pattern can be described by a hierarchical structure the pattern classes. of subpatterns analogous to the syntactic structure of – Statistical approach: languages. This permits application of formal language theory to the pattern recognition problem. • It is formulated and derived in a statistical framework. • Example: Bayes classification rule and its variations. – This approach is particularly useful in dealing with This rule yields an optimum classifier when the patterns which cannot be conveniently described by probability density function of each pattern population numerical measurements or are so complex that local and the probability of occurrence of each pattern class features cannot be identified and global properties are known. must be used. Examples of Automatic Pattern Recognition Systems • In a supervised learning environment, the • Character Recognition: system is taught to recognize patterns by means – Technique Used: Rather than being of various adaptive schemes. The essentials of compared with pre-stored patterns, hand- this approach are a set of training patterns of printed characters are analyzed as known classification and the implementation of combinations of common features, such as an appropriate learning procedure. curved lines, vertical and horizontal lines, • The unsupervised pattern recognition corners, and intersections. techniques are applicable to the situations where only a set of training patterns of unknown classification may be available. • Automatic Classification of Remotely • Biomedical Applications: Sensed Data: – Technique Used: Pattern primitives, such as – Examples: Land use, crop inventory, crop- long arcs, short arcs, and semi-straight disease detection, forestry, monitoring of air segments, which characterize the and water quality, geological and chromosome boundaries are defined. When geographical studies, and weather prediction, combined, these primitives form a string or plus a score of other applications of symbol sentence which can be associated environmental significance. with a so-called pattern grammar. There is – Technique Used: Bayes classifier one grammar for each type (class) of chromosome. • Nuclear Reactor Component Surveillance: – Technique Used: • Detect the clusters of pattern vectors by iterative • Fingerprint Recognition: applications of a cluster-seeking algorithm. – Technique Used: It detects tentative minutiae • The data cluster centers and associated and records their precise locations and descriptive parameters, such as cluster angles. 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 Recognition Model • A simple scheme for pattern recognition • We assume that the a priori probabilities consists of two basic components: for the occurrence of each class are equal, – Sensor: It is a device which converts a that is, it is just as likely that x comes from physical sample to be recognized into a set of one class as from another. 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.
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