Apparatus and method for classifying feature data at a high speed
First Claim
1. A method for classifying a plurality of feature data elements into a plurality of classes, each feature data element being represented by an object feature vector in a feature space, comprising the steps of:
- storing a plurality of reference feature vectors in a memory, each reference feature vector being predetermined;
dividing said feature space into a plurality of partial feature spaces using said plurality of reference feature vectors in response to a classification command, each partial feature space corresponding to one class of said plurality of classes, each object feature vector being assigned to one of said plurality of partial feature spaces in accordance with components of each object feature vector utilizing the sign of a single polynomial equation to determine which of two reference feature vectors is closer to each object feature vector;
determining an average feature vector from object feature vectors for every partial feature space; and
storing each average feature vector as a reference feature vector in said memory and repeating said dividing and assigning step until each average feature vector falls within a predetermined error.
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Abstract
In a feature data processing apparatus, one of two designated reference density vectors di and dj, to which a feature vector x corresponding to a feature element is closer, is determined from dij of the equation:
dij=(di-dj)·x-(di-dj)/2
This value is calculated for all the combinations of two reference feature vectors di and dj selected from a reference feature vector group dk (k=0 to n-1), thereby obtaining classification data. The classification data is determined, using a logical formula or a reference table. An average value of the components is calculated from the classification data and the components of all the feature vectors. The above operation is repeated until the calculated average and the components of the reference density vector converge within a predetermined allowance, thereby precisely classifying feature data.
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Citations
29 Claims
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1. A method for classifying a plurality of feature data elements into a plurality of classes, each feature data element being represented by an object feature vector in a feature space, comprising the steps of:
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storing a plurality of reference feature vectors in a memory, each reference feature vector being predetermined; dividing said feature space into a plurality of partial feature spaces using said plurality of reference feature vectors in response to a classification command, each partial feature space corresponding to one class of said plurality of classes, each object feature vector being assigned to one of said plurality of partial feature spaces in accordance with components of each object feature vector utilizing the sign of a single polynomial equation to determine which of two reference feature vectors is closer to each object feature vector; determining an average feature vector from object feature vectors for every partial feature space; and storing each average feature vector as a reference feature vector in said memory and repeating said dividing and assigning step until each average feature vector falls within a predetermined error. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A feature data processing apparatus for classifying object feature vectors representing feature data elements into classes, said feature data processing apparatus comprising:
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first memory means for storing a plurality of reference feature vectors which are predetermined; second memory means for storing said object feature vectors; assignment data determining means for determining assignment data for each of said object feature vectors in response to an input classification command, said assignment data including a plurality of assignment data portions, each assignment data portion indicating which of two reference feature vectors, selected from said plurality of reference feature vectors, is closer to each of said object feature vectors based on the sign of a single polynomial equation; classifying means for classifying said object feature vectors into one of said classes in accordance with assignment data associated with said object feature vectors, said classes respectively corresponding to said reference feature vectors; and average feature vector determining means for determining an average feature vector of object feature vectors of every class. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22)
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23. A method for classifying an object feature vector representing feature data, comprising the steps of:
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storing a reference feature vector group including a plurality of reference feature vectors in a memory, each reference feature vector being predetermined; determining assignment data of said object feature vector for combinations of two reference feature vectors sequentially selected from said memory in response to a classification command to obtain classification data in accordance with said assignment data, said assignment data including a plurality assignment data portions which indicate which of said two reference feature vectors of each combination is closer to said object feature vector based on the sign of a single polynomial equation; and assigning said object feature vector to one of a plurality of categories in accordance with said classification data. - View Dependent Claims (24, 25, 26, 27, 28, 29)
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Specification