Method of generating dictionary for pattern recognition and pattern recognition method using the same

A method of generating a dictionary for pattern recognition in which a feature vector of an input pattern is compared with a reference pattern vector of each class, read out of a pattern recognition dictionary, and a class of the reference vector pattern closest to said input pattern is output as the recognized result of said input pattern, said method comprising the steps of:

• a) extracting features of each of a plurality of training patterns each belonging to one of a plurality of predetermined classes and expressing said features of each training pattern as a vector;
• b) obtaining, as said reference pattern vector, the mean of said feature vectors of said training patterns of each class;
• c) discriminating each of said training patterns of all classes through use of an original distance function using said reference pattern vectors corresponding to each class to obtain, for said each class, a rival pattern set containing patterns misclassified as belonging to said each class;
• d) defining a discriminant function which contains, as first and second variables, a vector component obtained by projecting the difference between respectively corresponding components of a feature vector of a training pattern and a reference pattern vector into a subspace defined by the eigenvector of a covariance matrix of said class and the square of said vector component, and determining, for each class, parameters of said discriminant function so that a discrimination is made between patterns forming a training pattern set of said each class and patterns forming said rival pattern set for said each class;
• and
• e) writing, for said each class, parameter information into dictionary means, said parameter information corresponding to the parameters of said original distance function containing said reference pattern vector of said each class and parameters of said discriminant function determined for said each class.

A pattern recognition method which discriminates an input pattern by referring to dictionary means, and in which a discriminant function is predefined which includes, as first and second variables, the difference between respective corresponding components of a feature vector of each input pattern and the corresponding reference pattern vector and the square of said difference, and said dictionary means has stored therein, for each class, parameter information corresponding to parameters of said discriminant function and parameters of an original distance function, said parameters of said discriminant function being determined so that a discrimination is made by said discriminant function between a set of training patterns of each class and a set of rival patterns of said each class, said rival patterns set including patterns misclassified for each class when all training patterns of all classes are discriminated with said original distance function, and said parameters of said original distance function including said reference pattern vector of each class, said method comprising the steps of:

• a) extracting a feature vector of said input pattern;
• b) calculating, for each class, the distance between said feature vector of said input pattern and said reference pattern vector by applying said parameter information of each class, read out of said dictionary means, to a learned distance function formed by said original distance function and said discriminant function additively coupled by a predetermined coupling coefficient;
• and
• c) determining, as the class to which said input pattern belongs, the class in which to minimize said distance.

A pattern recognition method which discriminates an input pattern by referring to dictionary means, and in which a discriminant function is predefined which includes, as first and second variables, a component of a vector and the square of said component, said component of said vector being obtained by projecting the difference between respective corresponding components of a feature vector of each input pattern and the corresponding reference pattern vector into a subspace defined by an eigenvector of a covariance matrix of each class, and said dictionary means has stored therein, for each class, parameter information corresponding to parameters of said discriminant function and parameters of an original distance function, said parameters of said discriminant function being determined so that a discrimination is made by said discriminant function between a set of training patterns of each class and a set of rival patterns of said each class, said rival pattern set including patterns misclassified for each class when all training patterns of all classes are discriminated with said original distance function, and said parameters of said original function including said reference pattern vector of each class, said method comprising the steps of:

• a) extracting a feature vector of said input pattern;
• b) calculating, for each class, the distance between said feature vector of said input pattern and said reference pattern vector by applying said parameter information of each class, read out of said dictionary means, to a learned distance function formed by said original distance function and said discriminant function additively coupled by a predetermined coupling coefficient;
• and
• c) determining, as the class to which said input pattern belongs, the class in which to minimize said distance.

A pattern recognition method which discriminates an input pattern by referring to an original dictionary and a discriminant dictionary and in which a discriminant function is predefined which includes, as first and second variables, a component of a vector and the square of said component, said component of said vector being obtained by projecting the difference between respective corresponding components of a feature vector of each pattern and the corresponding reference pattern vector into a subspace defined by an eigenvector of a covariance matrix of each class, said original dictionary has stored therein, for each class, parameters of an original distance function, and said discriminant dictionary has stored therein parameters of said discriminant function, said parameters of said discriminant function being determined so that a discrimination is made by said discriminant function between a set of training patterns of each class and a set of rival patterns of said each class, said rival pattern set including patterns misclassified for each class when all training patterns of all classes are discriminated with said learned distance function, and said parameters of said original distance function including the reference pattern vector of each class, said method comprising the steps of:

• a) extracting a feature vector of said input pattern;
• b) calculating, for each class, the distance between said feature vector and said reference pattern vector of said input pattern by applying said parameters of each class, read out of said original dictionary, to said original distance function;
• c) selecting a plurality of said calculated distances in increasing order of distance and selecting, as class candidates, a plurality of classes corresponding to said plurality of selected distances;
• and
• d) calculating the value of said discriminant function by applying thereto said parameters read out of said discriminant dictionary for each of said class candidates and determining, as the class to which said input pattern belongs, the class candidate which provides the minimum value of said discriminant function.

A pattern recognition method which discriminates an input pattern by referring to an original dictionary and a discriminant dictionary and in which a discriminant function is predefined which includes, as first and second variables, the difference between respective corresponding components of a feature vector or each pattern and the corresponding reference pattern vector and the square of said difference, said original dictionary has stored therein, for each class, parameters of an original distance function, and said discriminant dictionary has stored therein parameters of said discriminant function, said parameters of said discriminant function being determined so that a discrimination is made by said discriminant function between a set of training patterns of each class and a set of rival patterns of training patterns of each class and a set of rival patterns of said each class, said rival pattern set including patterns misclassified for each class when all training patterns of all classes are discriminated with said learned distance function, and said parameters of said original distance function including the reference pattern vector of each class, said method comprising the steps of:

• a) extracting a feature vector of said input pattern;
• b) calculating, for each class, the distance between said feature vector and said reference pattern vector of said input pattern by applying said parameters of each class, read out of said distance dictionary, to said original distance function;
• c) selecting a plurality of said calculated distances in increasing order of distance and selecting, as class candidates, a plurality of selected distances;
• and
• d) calculating the value of said discriminant function by applying thereto said parameters read out of said discriminant dictionary for each of said class candidate and determining, as the class to which said input pattern belongs, the class candidate which provides the minimum value of said discriminant function.