Method and system for extracting features in a pattern recognition system
First Claim
1. A method of classifying an object represented by object signals containing a pattern of the object, the method comprising:
- creating a noise subspace for at least one object class;
transforming the object signals to produce an object vector;
projecting the object vector onto the noise subspace for each object class; and
determining that the object represented by the object signals is a member of the object class based on the projection of the object vector onto the noise subspace.
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Abstract
Method and system for extracting features from measurement signals obtained from real world, physical signals by first forming an invariant component of the measurement signals and then using a technique based on a noise subspace algorithm. This technique first casts or projects the transformed measurement signals into separate subspaces for each extraneous variation or group of variations. The subspaces have minimal over-lap. The recognition of a particular invariant component within a pertinent subspace is then preferably performed using Singular Value Decomposition (SVD) techniques to generate a pattern recognition signal. A series of transformations can be used to form an invariant component called the Scale and Translation Invariant Representation (STIR). In one embodiment, the first step is to form an appropriate time-frequency representation such as the Reduced Interference Distribution (RID) or other distribution whose properties are covariant with translations in time and frequency and changes in scale. A series of additional transformations including a scale transform yield the STIR representation. Features are then extracted from a set of STIR representations taken as examples of the desired signal. The STIR approach removes much of the variation due to translation. In bit-mapped documents, the same translation invariant and scale invariant transformations may be made to regularize characters and words. Also, the same feature selection method functions in an image setting. The method has been found to be particularly useful in word spotting in bitmapped documents corrupted by faxing.
155 Citations
27 Claims
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1. A method of classifying an object represented by object signals containing a pattern of the object, the method comprising:
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creating a noise subspace for at least one object class;
transforming the object signals to produce an object vector;
projecting the object vector onto the noise subspace for each object class; and
determining that the object represented by the object signals is a member of the object class based on the projection of the object vector onto the noise subspace. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
determining that the object is a member of a font object class based on the projection of the object vector onto the noise subspace of at least one font class; and
determining that the object is a member of a character class based on the projection of the object vector onto the noise subspace of at least one character class.
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9. A method of classifying an object as in claim 1 wherein transforming comprises obtaining the reduced interference distribution (RID).
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10. A method of classifying an object as in claim 1 wherein creating a noise subspace comprises:
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obtaining a plurality of exemplar signals;
forming a vector representation for each exemplar signal;
forming an invariant transform matrix from the vector representations, the matrix having a greater number of elements in each vector than the number of vector representations;
obtaining a singular value decomposition (SVD) of the matrix; and
defining the noise subspace as eigenvectors not forming a complete orthonormal basis.
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11. A method of classifying an object as in claim 1 wherein the object signals comprise an image containing the object.
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12. A method of classifying an object as in claim 1 further comprising generating an energy distribution signal corresponding to spectral energy content of the object signals and wherein transforming is performed on the energy distribution signal.
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13. A method of pattern recognition comprising:
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generating at least one noise subplane vector;
measuring signals representative of a pattern;
performing at least one invariant transformation on the measured signals;
measuring the orthogonality of the transformed signals and each noise subplane vector; and
recognizing the pattern based on the measured orthogonality between the transformed signals and each noise subplane vector. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26)
determining that the pattern is a member of a particular font based on the measured orthogonality of the transformed signals and noise subplane vectors for each font; and
determining that the pattern is a particular character based on the measured orthogonality of the transformed signals and noise subplane vectors for each character in the particular font.
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23. A method of pattern recognition as in claim 13 wherein performing at least one invariant transformation comprises obtaining the reduced interference distribution (RID).
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24. A method of pattern recognition as in claim 13 wherein generating at least one noise subplane vector comprises:
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obtaining a plurality of exemplar signals;
forming a vector representation for each exemplar signal;
forming an invariant transform matrix from the vector representations, the matrix having a greater number of elements in each vector than the number of vector representations;
obtaining a singular value decomposition (SVD) of the matrix; and
defining each noise subplane vector as an eigenvector not forming a complete orthonormal basis.
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25. A method of pattern recognition as in claim 13 wherein the signals representative of a pattern comprise an image containing the pattern.
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26. A method of pattern recognition as in claim 13 further comprising generating an energy distribution signal corresponding to spectral energy content of the signals representative of a pattern and wherein the at least one invariant transformation is performed on the energy distribution signal.
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27. A system for classifying an object represented by object signals containing a pattern of the object, the system comprising:
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means for creating a noise subspace for at least one object class;
means for transforming the object signals to produce an object vector;
means for projecting the object vector onto the noise subspace for each object class; and
means for determining that the object represented by the object signals is a member of the object class based on the projection of the object vector onto the noise subspace.
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Specification