Statistical pattern recognition: A review

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Statistical pattern recognition: A review (2000)

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by Anil K. Jain , Robert P. W. Duin , Jianchang Mao

Venue:	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Citations:	1034 - 30 self

BibTeX

@ARTICLE{Jain00statisticalpattern,
    author = {Anil K. Jain and Robert P. W. Duin and Jianchang Mao},
    title = {Statistical pattern recognition: A review},
    journal = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE},
    year = {2000},
    volume = {22},
    number = {1},
    pages = {4--37}
}

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Abstract

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have bean receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

Keyphrases

statistical pattern recognition pattern recognition statistical approach arbitrary orientation review paper feature extraction recognition system data mining research topic following issue web searching various stage test sample multimedia data challenging field pattern class neural network technique general problem various framework pattern recognition system unsupervised classification performance evaluation careful attention complex pattern face recognition efficient pattern recognition technique well-known method cursive handwriting recognition pattern representation primary goal cluster analysis

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