LICENSE PLATE RECOGNITION WITH LOW-RANK, SHARED CHARACTER CLASSIFIERS
First Claim
1. A method to perform multiple classification of an image simultaneously using multiple classifiers, where information between the classifiers is shared explicitly and is achieved with a low-rank decomposition of the classifier weights, the method comprising:
- acquiring an input image;
extracting a representation from the input image;
applying the low-rank character classifiers to the extracted image representation, including;
multiplying the extracted image representation by |Σ
| embedding matrices Ŵ
c to generate a latent representation of d-dimensions for each of the |Σ
| characters, wherein the embedding matrices are uncorrelated with a position of the extracted character;
projecting the latent representation with a decoding matrix shared by all the character embedding matrices to generate scores of every character in an alphabet at every position;
wherein at least one of the multiplying the extracted representation from the input and the projecting the latent representation with the decoding matrix are performed with a processor.
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Abstract
A method is disclosed for performing multiple classification of an image simultaneously using multiple classifiers, where information between the classifiers is shared explicitly and is achieved with a low-rank decomposition of the classifier weights. The method includes applying an input image to classifiers and, more particularly, multiplying the extracted input image features by |Σ| embedding matrices Ŵc to generate a latent representation of d-dimensions for each of the |Σ| characters. The embedding matrices are uncorrelated with a position of the extracted character. The step of applying the extracted character to the classifiers further includes projecting the latent representation with a decoding matrix shared by all the character embedding matrices to generate scores of every character in an alphabet at every position. At least one of the multiplying the extracted input image features and the projecting the latent representation with the decoding matrix are performed with a processor.
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Citations
20 Claims
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1. A method to perform multiple classification of an image simultaneously using multiple classifiers, where information between the classifiers is shared explicitly and is achieved with a low-rank decomposition of the classifier weights, the method comprising:
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acquiring an input image; extracting a representation from the input image; applying the low-rank character classifiers to the extracted image representation, including; multiplying the extracted image representation by |Σ
| embedding matrices Ŵ
c to generate a latent representation of d-dimensions for each of the |Σ
| characters, wherein the embedding matrices are uncorrelated with a position of the extracted character;projecting the latent representation with a decoding matrix shared by all the character embedding matrices to generate scores of every character in an alphabet at every position; wherein at least one of the multiplying the extracted representation from the input and the projecting the latent representation with the decoding matrix are performed with a processor. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for performing multiple classification of an image simultaneously using multiple classifiers, where information between the classifiers is shared explicitly and is achieved with a low-rank decomposition of the classifier weights, the system comprising:
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a processor; and a non-transitory computer readable memory storing instructions that are executable by the processor to; acquire an input image; extract a character from the input image; applying the extracted character to at least one classifier; a classifier, including; |Σ
| embedding matrices Ŵ
c each uncorrelated with a position of the extracted character, wherein the processor multiplies the extracted input image representation by the |Σ
| embedding matrices Ŵ
c to generate a latent representation of d-dimensions for each of the |Σ
| characters; and
,a decoding matrix shared by all the character embedding matrices, wherein the processor projects the latent representation with the decoding matrix to generate scores of every character in an alphabet at every position. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification