Image analysis – Learning systems – Neural networks
Reexamination Certificate
1995-10-24
2001-05-15
Chang, Jon (Department: 2623)
Image analysis
Learning systems
Neural networks
C382S226000
Reexamination Certificate
active
06233352
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to an information processing method and an apparatus relating to a classification method for classifying patterns such as images, sounds and characters into categories.
The present invention further relates to an information processing method and an apparatus suitable for recognizing information of images, sounds and characters.
2. Related Background Art
In a prior art method of pattern recognition, a recognition method using a neural network has been known. A great feature of the neural network resides in that a powerful learn algorithm represented by an error inverse propagation algorithm is provided. Because of this feature, the neural network has been adopted in a wide field of pattern recognition.
Another method for pattern recognition is a method for classifying a pattern into a category by using classification trees stepwise. For example, in the pattern recognition system disclosed in JP-B-6-52537, characteristic axes are numbered and they are categorized in accordance with the numbers.
A method for categorizing based on primary coupling of characteristic variables is also known. In general, a better result is provided by using the primary coupling of characteristic variables than using the characteristic axes one by one.
However, the above prior art techniques have the following defects.
1. The range of the characteristic variables to which the neural network is applicable is in the order of 10, and when input variables include higher order variables, some category pre-separation or character extraction is needed. In addition, when pre-processing such as category pre-separation or character extraction is conducted, an error may be included during the pre-processing and a final recognition rate is not that high even if the neural network is constructed with a high precision.
2. The range of character variables to which the classification trees are applicable is also in the order of 10, and when higher order characteristic variables are to be processed, the construction of the classification trees is virtually impossible.
3. In the actual pattern recognition, the orders of character variables of unprocessed data ranges between 100 and 1000. Thus, it is impossible to use the existing neural network and the classification trees which allow only the order of 10 to the actual pattern recognition as they exist.
SUMMARY OF THE INVENTION
The present invention comprises a hierarchical pre-processing step for hierarchically pre-processing input learning patterns and a classification tree preparation step for preparing a classification tree based on the learning patterns processed in the hierarchical pre-processing step. As a result, a high recognition factor is attained with a high efficiency even if input variables possess high order characteristics.
The present invention attains high efficiency categorization by degenerating the characteristic variables of the learning patterns stepwise in the hierarchical pre-processing step.
The present invention comprises a developed variable discrimination step for selecting variables in accordance with a relation between a higher hierarchy and a lower hierarchy in the classification tree preparation step, and develops the degenerated variables toward the lower hierarchy based on a result of the developed variable discrimination step to attain the efficient categorization.
The present invention recognizes the input pattern by using the prepared classification tree to attain the high recognition factor efficiently.
The present invention prepares a recognition template based on the prepared classification tree and recognizes the input pattern by using the template to attain the high recognition rate efficiently.
The present invention allows the recognition of hand-written characters efficiently at a high recognition factor by inputting a hand-written character pattern as the input pattern.
The present invention allows the recognition of a optically read character pattern efficiently at a high recognition factor by inputting the optically read character pattern as an input pattern.
The present invention allows the recognition of sound efficiently at a high recognition factor by inputting a sound pattern as an input pattern.
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Canon Kabushiki Kaisha
Chang Jon
Fitzpatrick ,Cella, Harper & Scinto
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