(computer science) The automatic identification of figures, characters, shapes, forms, and patterns without active human participation in the decision process.
| Sci-Tech Dictionary: pattern recognition |
(computer science) The automatic identification of figures, characters, shapes, forms, and patterns without active human participation in the decision process.
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1. Introduction
Since our early childhood, we have been observing patterns in the objects around us (e.g. toys, flowers, pets, and faces). Learning patterns also reinforces, and is reinforced by, the acquisition of language. By the time children are 5 years old, most can recognize digits and letters. Small and large characters, handwritten and machine-printed characters, characters of different colours and orientations, and partially occluded letters — all are easily recognized by the young. We take this ability for granted until we face the task of teaching a machine how to recognize the characters. In spite of almost 50 years of research, design of general-purpose machines for pattern recognition remains an elusive goal.2. Pattern recognition and classification
Pattern recognition aims to make the process of learning and detection of patterns explicit, such that it can be partially or entirely implemented on computers. Automatic (machine) recognition, description, and classification (grouping of patterns into pattern classes) have become important problems in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing. In almost any area of science in which observations are studied but the underlying mathematical or statistical models are not available, pattern recognition can be used to support human concept acquisition or decision making. Given a group of objects, there are two ways to build a classification or recognition system (Watanabe 1985): supervised, i.e. with a teacher, or unsupervised, without the help of a teacher (see Fig. 4).
3. Systems for automatic pattern recognition
Rapid advances in computing technology not only enable us to process huge amounts of data, but also facilitate the use of elaborate and diverse methods for data analysis and classification. At the same time, demands on automatic pattern recognition systems are rising enormously due to the availability of large databases and stringent performance requirements (faster recognition speed and higher accuracy at a lower cost). In many emerging applications, it is clear that no single approach for classification is 'optimal' and multiple methods and approaches have to be used. Consequently, combining several sensing modalities and classifiers is now a common practice in pattern recognition.
4. Some challenges in pattern and class learning
Selection of training sets. If we want to learn from examples, care should be paid to the way the examples are selected. For instance, a system for the recognition of electrocardiograms (say, into normal heart vs. diseased heart) can be based on examples collected in hospitals, on examples collected in a general screening test, on typical cardiograms that are clear examples of particular classes of heart problems, or on selected cardiograms that are the border cases between these classes. The choice of such a strategy is strongly related to the learning approach to be used and to the way the recognition system can be used.Representation of objects. There are various ways to represent objects: raw data measurements (e.g. overall height, overall weight), derived measurements or features (e.g. ratio of height to weight), a structural description (e.g. height to weight ratios of parts of bodies and spatial relationship of the body parts), etc. In the statistical approach, the feature representation is the most common. For the recognition of simple real-world objects, the features can be their sizes, shapes, colours, etc. More features do not necessarily imply a better classification performance. Given a representation scheme, an objective measure (e.g. 'distance' or 'score') needs to be defined to quantify the (dis)similarity between any two representations.Inter-and intra-class distances. A direct and intuitive way to see whether a feature representation is good for a classification problem is to compare the inter-class distances (e.g. between the two sets of pictures of two different persons) with the intra-class distances (e.g. between all pictures of a single person). If the inter-class distances are much larger than the intra-class distances, the classification problem is easy. If they are of similar orders, either the classes overlap, or a more advanced procedure is needed to separate the classes. Obviously, a representation with large inter-class variability and small intra-class variability is desirable. See Fig. 7 for an illustration on inter-and intra-class distances.Invariance of representation. Some object variations may not be important for the classification task, e.g. the size of a character, the angle (pose) at which a face is observed, the speed by which a word is spoken. These variations may influence the representation so that the position of the object in feature space is changed. An important problem is how to identify and extract these so-called invariants. We can collect objects under all possible variations, which is expensive. A preferred approach is to use invariant features.The problem of overtraining. An overly complex pattern recognition system may learn unnecessary details of training samples of a pattern and, consequently, will be unable to recognize the essential commonality defining the pattern. It is necessary to adapt the complexity of the recognition system to the complexity and size of the data set under consideration.

5. Pattern recognition applications
Pattern recognition is used in any area of science and engineering that studies the structure of observations. It is now frequently used in many applications in manufacturing industry, healthcare, and the military. Examples include the following.— Anil K. Jain/Robert P. W. Duin
| Wikipedia: Pattern recognition |
Pattern recognition is "the act of taking in raw data and taking an action based on the category of the pattern"[1]. Most research in pattern recognition is about methods for supervised learning and unsupervised learning.
Pattern recognition aims to classify data (patterns) based either on a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space. This is in contrast to pattern matching, where the pattern is rigidly specified.
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A complete pattern recognition system consists of a sensor that gathers the observations to be classified or described, a feature extraction mechanism that computes numeric or symbolic information from the observations, and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features.
The classification or description scheme is usually based on the availability of a set of patterns that have already been classified or described. This set of patterns is termed the training set, and the resulting learning strategy is characterized as supervised learning. Learning can also be unsupervised, in the sense that the system is not given an a priori labeling of patterns, instead it itself establishes the classes based on the statistical regularities of the patterns.
The classification or description scheme usually uses one of the following approaches: statistical (or decision theoretic) or syntactic (or structural). Statistical pattern recognition is based on statistical characterizations of patterns, assuming that the patterns are generated by a probabilistic system. Syntactical (or structural) pattern recognition is based on the structural interrelationships of features. A wide range of algorithms can be applied for pattern recognition, from simple naive Bayes classifiers to powerful neural networks.
Pattern recognition is more complex when templates are used to generate variants. For example, in English, sentences often follow the "N-VP" (noun - verb phrase) pattern, but some knowledge of the English language is required to detect the pattern. Pattern recognition is studied in many fields, including psychology, ethology, cognitive science and computer science.
Holographic associative memory is another type of pattern matching scheme where a target small patterns can be searched from a large set of learned patterns based on cognitive meta-weight.
Within medical science, pattern recognition is the basis for computer-aided diagnosis (CAD) systems. CAD describes a procedure that supports the doctor's interpretations and findings.
Typical applications are automatic speech recognition, classification of text into several categories (e.g. spam/non-spam email messages), the automatic recognition of handwritten postal codes on postal envelopes, or the automatic recognition of images of human faces. The last two examples form the subtopic image analysis of pattern recognition that deals with digital images as input to pattern recognition systems.[2][3]
This article was originally based on material from the Free On-line Dictionary of Computing, which is licensed under the GFDL.
This entry is from Wikipedia, the leading user-contributed encyclopedia. It may not have been reviewed by professional editors (see full disclaimer)
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