Kernelized spatial-contextual image classification
First Claim
1. A method for categorizing a plurality of unlabeled images, the method comprising:
- on a computing device;
generating a first spatial-contextual model to represent a first image, the first spatial-contextual model having a plurality of interconnected nodes arranged in a first pattern of connections with each node connected to at least one other node;
generating a second spatial-contextual model to represent a second image, the second spatial-contextual model having a plurality of interconnected nodes arranged in the first pattern of connections;
determining a relationship of adjacent nodes that each node is connected with in the first pattern of connections; and
calculating a distance between corresponding nodes in the first spatial-contextual model and the second spatial-contextual model based on the relationship of adjacent connected nodes to determine a distance between the first image and the second image.
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Accused Products
Abstract
Kernelized spatial-contextual image classification is disclosed. One embodiment comprises generating a first spatial-contextual model to represent a first image, the first spatial-contextual model having a plurality of interconnected nodes arranged in a first pattern of connections with each node connected to at least one other node, generating a second spatial-contextual model to represent a second image using the first pattern of connections, and estimating the distance between corresponding nodes in the first spatial-contextual model and the second spatial-contextual model based on a relationship with adjacent connected nodes to determine a distance between the first image and the second image.
13 Citations
20 Claims
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1. A method for categorizing a plurality of unlabeled images, the method comprising:
on a computing device; generating a first spatial-contextual model to represent a first image, the first spatial-contextual model having a plurality of interconnected nodes arranged in a first pattern of connections with each node connected to at least one other node; generating a second spatial-contextual model to represent a second image, the second spatial-contextual model having a plurality of interconnected nodes arranged in the first pattern of connections; determining a relationship of adjacent nodes that each node is connected with in the first pattern of connections; and calculating a distance between corresponding nodes in the first spatial-contextual model and the second spatial-contextual model based on the relationship of adjacent connected nodes to determine a distance between the first image and the second image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computer-readable storage medium comprising stored instructions executable by a computing device to categorize a plurality of unlabeled images, the stored instructions executed by the computing device to perform a method comprising:
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generating a first spatial-contextual model to represent a first image, the first spatial-contextual model having a plurality of interconnected nodes arranged in a first pattern of connections with each node connected to at least one other node; generating a second spatial-contextual model to represent a second image, the second spatial-contextual model having a plurality of interconnected nodes arranged in the first pattern of connections; training a Universal Reference Model from a plurality of referential images; adapting the first spatial-contextual model and the second spatial-contextual model to the Universal Reference Model; determining a relationship of adjacent nodes that each node is connected with in the first pattern of connections; and calculating a distance between corresponding nodes in the first spatial-contextual model and the second spatial-contextual model based on the relationship of adjacent connected nodes. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A system to categorize a plurality of unlabeled images, the system comprising:
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an input; a memory including stored instructions; and a processor in communication with the input and the memory, the processor being configured to executed the stored instructions to implement; a model generation module configured to generate a first dependency tree hidden Markov model to represent a first image, the model generation module further configured to generate a second dependency tree hidden Markov model to represent a second image; a distance module in communication with the model generation module, the distance module configured to calculate an image distance between the first dependency tree hidden Markov model and the second dependency tree hidden Markov model; and a categorization module to receive the image distance and to generate a kernelized spatial-contextual model using the image distance. - View Dependent Claims (20)
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Specification