Pyramid match kernel and related techniques
First Claim
1. A method for classifying or comparing data objects comprising:
- detecting points of interest within two data objects;
computing feature descriptors at said points of interest;
forming a multi-resolution histogram over feature descriptors for each data object; and
computing a weighted intersection of multi-resolution histogram for each data object.
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Abstract
A method for classifying or comparing objects includes detecting points of interest within two objects, computing feature descriptors at said points of interest, forming a multi-resolution histogram over feature descriptors for each object and computing a weighted intersection of multi-resolution histogram for each object. An alternative embodiment includes a method for matching objects by defining a plurality of bins for multi-resolution histograms having various levels and a plurality of cluster groups, each group having a center, for each point of interest, calculating a bin index, a bin count and a maximal distance to the bin center and providing a path vector indicative of the bins chosen at each level. Still another embodiment includes a method for matching objects comprising creating a set of feature vectors for each object of interest, mapping each set of feature vectors to a single high-dimensional vector to create an embedding vector and encoding each embedding vector with a binary hash string.
312 Citations
20 Claims
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1. A method for classifying or comparing data objects comprising:
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detecting points of interest within two data objects; computing feature descriptors at said points of interest; forming a multi-resolution histogram over feature descriptors for each data object; and computing a weighted intersection of multi-resolution histogram for each data object. - View Dependent Claims (2, 3, 4)
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5. A method for assessing data objects comprising:
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characterizing an data object by a set of feature vectors; partitioning feature space into a plurality of bins with multiple levels with the size of the bins changing at each level; computing a histogram over the partitioned feature space using the set of feature vectors for the data object; and approximating the similarity to another data object according to partial matching in similar feature space. - View Dependent Claims (6, 7, 8, 9)
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10. A method for matching data objects comprising:
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characterizing each data object by a set of feature vectors; partitioning feature space into a plurality irregularly shaped and sized bins with multiple levels and forming a pyramid shape; encoding point sets of interest from the feature vectors into multi-resolution histograms; providing a matching value indicative of the probability of a match among data objects from any two histogram pyramids. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
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18. A method for matching objects comprising:
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creating a set of feature vectors for each object of interest; mapping each set of feature vectors to a single high-dimensional vector to create an embedding vector; and encoding each embedding vector with a binary hash string. - View Dependent Claims (19, 20)
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Specification