FEATURE CONVERSION DEVICE, SIMILAR INFORMATION SEARCH APPARATUS PROVIDED THEREWITH, CODING PARAMETER GENERATION METHOD, AND COMPUTER PROGRAM
First Claim
1. A feature conversion device comprising:
- a learning pair selector that inputs a plurality of learning feature vectors thereto and selects a plurality of pairs of learning feature vectors from the learning feature vectors;
a bit code converter that transforms the learning feature vector using a transformation matrix and converts the transformed learning feature vector into a bit code;
a cost function calculator that calculates a cost function indicating a sum of differences between a distance between the learning feature vectors input to the learning pair selector and a distance between the bit codes into which the transformed learning feature vectors are converted by the bit code converter with respect to all the pairs of learning feature vectors; and
a transformation matrix update unit that selects an element of the transformation matrix used in the bit code converter and substitutes a substitution candidate for the selected element to update the transformation matrix,wherein the bit code converter transforms the learning feature vector using the transformation matrix updated by the transformation matrix update unit and converts the transformed learning feature vector into a bit code,the cost function calculator fixes the element by selecting one element from the substitution candidate and the original element based on the cost function when the transformation matrix update unit substitutes the substitution candidate for the element of the transformation matrix,the transformation matrix update unit selects the element while sequentially changing the elements and the cost function calculator fixes the selected element every time the transformation matrix update unit selects the element, thereby finally fixing the optimum transformation matrix, andthe substitution candidate is specified such that a speed of transformation processing that the bit code converter performs using the transformation matrix is enhanced.
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Abstract
A bit code converter transforms a learning feature vector using a transformation matrix updated by a transformation matrix update unit, and converts the transformed learning feature vector into a bit code. When the transformation matrix update unit substitutes a substitution candidate for an element of the transformation matrix, a cost function calculator fixes the substitution candidate that minimizes a cost function as the element. The transformation matrix update unit selects the element while sequentially changing the elements, and the cost function calculator fixes the selected element every time the transformation matrix update unit selects the element, thereby finally fixing the optimum transformation matrix. A substitution candidate specifying unit specifies the substitution candidate such that a speed of transformation processing that the bit code converter performs using the transformation matrix using the transformation matrix is enhanced based on a constraint condition stored in a constraint condition storage unit.
7 Citations
17 Claims
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1. A feature conversion device comprising:
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a learning pair selector that inputs a plurality of learning feature vectors thereto and selects a plurality of pairs of learning feature vectors from the learning feature vectors; a bit code converter that transforms the learning feature vector using a transformation matrix and converts the transformed learning feature vector into a bit code; a cost function calculator that calculates a cost function indicating a sum of differences between a distance between the learning feature vectors input to the learning pair selector and a distance between the bit codes into which the transformed learning feature vectors are converted by the bit code converter with respect to all the pairs of learning feature vectors; and a transformation matrix update unit that selects an element of the transformation matrix used in the bit code converter and substitutes a substitution candidate for the selected element to update the transformation matrix, wherein the bit code converter transforms the learning feature vector using the transformation matrix updated by the transformation matrix update unit and converts the transformed learning feature vector into a bit code, the cost function calculator fixes the element by selecting one element from the substitution candidate and the original element based on the cost function when the transformation matrix update unit substitutes the substitution candidate for the element of the transformation matrix, the transformation matrix update unit selects the element while sequentially changing the elements and the cost function calculator fixes the selected element every time the transformation matrix update unit selects the element, thereby finally fixing the optimum transformation matrix, and the substitution candidate is specified such that a speed of transformation processing that the bit code converter performs using the transformation matrix is enhanced. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A coding parameter generating method for outputting a transformation matrix as a coding parameter used to convert a feature vector into a bit code, the coding parameter generating method comprising:
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a first step of selecting a plurality of pairs of learning feature vectors from a plurality of learning feature vectors; a second step of initializing the transformation matrix under a constraint condition; a third step of calculating a cost function indicating a sum of differences between a distance between the learning feature vectors and a distance between the bit codes into which the learning feature vectors are converted using the initialized transformation matrix with respect to the plurality of pairs of learning feature vectors; a fourth step of selecting an element set to a substitution target from elements of the transformation matrix; a fifth step of substituting a particular substitution candidate for the element selected in the fourth step; a sixth step of calculating the cost function using the transformation matrix in which the substitution candidate is substituted for the selected element in the fifth step; a seventh step of determining whether all the substitution candidates are substituted for the selected element, and returning to the fifth step when all the substitution candidates are not substituted for the selected element; an eighth step of tentatively fixing an optimum transformation matrix by fixing the substitution candidate having the smallest calculated cost function in the substitution candidates as the selected element when all the substitution candidates are substituted for the selected element in the seventh step; a ninth step of determining whether the optimum transformation matrix tentatively fixed in the eighth step converges, and returning to the fourth step when the optimum transformation matrix does not converge; and a tenth step of outputting the tentatively-fixed optimum transformation matrix as a finally-fixed optimum transformation matrix when the optimum transformation matrix tentatively fixed in the eighth step converges. - View Dependent Claims (17)
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Specification