TAGGING OVER TIME: REAL-WORLD IMAGE ANNOTATION BY LIGHTWEIGHT METALEARNING
First Claim
1. A method of annotating an image, comprising the steps of:
- receiving one or more annotations of an image from an existing, black box image annotation system;
providing additional annotations of the image using the annotations provided by the black box system and other available resources;
computing the probability that each additional annotation is an accurate annotation for the image; and
annotating the image using those annotations having the highest probability.
2 Assignments
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Accused Products
Abstract
A principled, probabilistic approach to meta-learning acts as a go-between for a ‘black-box’ image annotation system and its users. Inspired by inductive transfer, the approach harnesses available information, including the black-box model'"'"'s performance, the image representations, and a semantic lexicon ontology. Being computationally ‘lightweight.’ the meta-learner efficiently re-trains over time, to improve and/or adapt to changes. The black-box annotation model is not required to be re-trained, allowing computationally intensive algorithms to be used. Both batch and online annotation settings are accommodated. A “tagging over time” approach produces progressively better annotation, significantly outperforming the black-box as well as the static form of the meta-learner, on real-world data.
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Citations
20 Claims
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1. A method of annotating an image, comprising the steps of:
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receiving one or more annotations of an image from an existing, black box image annotation system; providing additional annotations of the image using the annotations provided by the black box system and other available resources; computing the probability that each additional annotation is an accurate annotation for the image; and annotating the image using those annotations having the highest probability. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification