Weighted feature voting for classification using a graph lattice
First Claim
1. A system for classifying a test image, said system comprising:
- at least one processor programmed to;
receive a data graph of the test image;
receive a graph lattice, the graph lattice including a plurality of nodes, each of the plurality of nodes including a subgraph, a weight and at least one mapping of the subgraph to data graphs of a plurality of training images, the plurality of training images corresponding to a plurality of classes;
map the data graph of the test image by the subgraphs of the plurality of nodes;
compare mappings between the graph lattice and the data graphs of the training images with mappings between the graph lattice and the data graph of the test image to determine, for each of the training images, a weighted vote of similarity between the data graph of the training image and the data graph of the test image, the weighted vote based on the weights of the plurality of nodes; and
,determine a class of the test image from the weighted votes of the training images, the class of the test image being the class of the training image with the highest weighted vote above a threshold number of votes.
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Abstract
A system and method classify a test image. At least one processor receives a data graph computed from the test image. Further, a graph lattice is received. The graph lattice includes a plurality of nodes, each including a subgraph, a weight and at least one mapping of the subgraph to data graphs of a plurality of training images. The training images correspond to a plurality of classes. The data graph of the test image is mapped by the subgraphs of the nodes. Mappings between the graph lattice and the data graphs of the training images are compared with mappings between the graph lattice and the data graph of the test image to determine weighted votes of similarity between the data graphs of the training images and the data graph of the test image. The class of the test image is determined from the weighted votes.
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Citations
20 Claims
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1. A system for classifying a test image, said system comprising:
at least one processor programmed to; receive a data graph of the test image; receive a graph lattice, the graph lattice including a plurality of nodes, each of the plurality of nodes including a subgraph, a weight and at least one mapping of the subgraph to data graphs of a plurality of training images, the plurality of training images corresponding to a plurality of classes; map the data graph of the test image by the subgraphs of the plurality of nodes; compare mappings between the graph lattice and the data graphs of the training images with mappings between the graph lattice and the data graph of the test image to determine, for each of the training images, a weighted vote of similarity between the data graph of the training image and the data graph of the test image, the weighted vote based on the weights of the plurality of nodes; and
,determine a class of the test image from the weighted votes of the training images, the class of the test image being the class of the training image with the highest weighted vote above a threshold number of votes. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method for classifying a test image, said method comprising:
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receiving by at least one processor a data graph of the test image; receiving by the at least one processor a graph lattice, the graph lattice including a plurality of nodes, each of the plurality of nodes including a subgraph, a weight and at least one mapping of the subgraph to data graphs of a plurality of training images, the plurality of training images corresponding to a plurality of classes; mapping by the at least one processor the data graph of the test image by the subgraphs of the plurality of nodes; comparing by the at least one processor mappings between the graph lattice and the data graphs of the training images with mappings between the graph lattice and the data graph of the test image to determine, for each of the training images, a weighted vote of similarity between the data graph of the training image and the data graph of the test image, the weighted vote based on the weights of the plurality of nodes; and
,determining by the at least one processor a class of the test image from the weighted votes of the training images, the class of the test image being the class of the training image with the highest weighted vote above a threshold number of votes. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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20. A system for classifying a test image, said system comprising:
at least one processor programmed to; receive a data graph of the test image; receive a graph lattice, the graph lattice including a plurality of nodes, each of the plurality of nodes including a subgraph, a weight and at least one mapping of the subgraph to data graphs of a plurality of training images, the plurality of training images corresponding to a plurality of classes; map the subgraphs of the plurality of nodes to the data graph of the test image; for each mapping between a node of the graph lattice and the data graph of the test image, compare the mapping of the test image with each mapping between the node of the graph lattice and the data graphs of the training images; based on the comparisons, determine, for each of the training images, a weighted vote of similarity between the data graph of the training image and the data graph of the test image, the weighted vote based on the weights of the plurality of nodes; and
,determine the class of the test image from the weighted votes of the training images, the class of the test image being the class of the training image with the highest weighted vote above a threshold number of votes.
Specification