COLLECTIVE MEDIA ANNOTATION USING UNDIRECTED RANDOM FIELD MODELS
First Claim
1. A method for detecting one or more concept in multimedia comprising:
- (a) extracting low level features representative of the one or more concept;
(b) training a discriminative classifier for each concept using a set of the low level features;
(c) building a collective annotation model combining each of the discriminative classifiers;
(d) defining one or more interaction potential to model interdependence between related concepts; and
(e) detecting the presence/absence of one or more concepts based on the collective annotation model and the defined interaction potentials.
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Abstract
In an embodiment, the present invention relates to a method for semantic analysis of digital multimedia. In an embodiment of the invention, low level features are extracted representative of one or more concepts. A discriminative classifier is trained using these low level features. A collective annotation model is built based on the discriminative classifiers. In various embodiments of the invention, the frame work is totally generic and can be applied with any number of low-level features or discriminative classifiers. Further, the analysis makes no domain specific assumptions, and can be applied to activity analysis or other scenarios without modification. The framework admits the inclusion of a broad class of potential functions, hence enabling multi-modal analysis and the fusion of heterogeneous information sources.
8 Citations
20 Claims
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1. A method for detecting one or more concept in multimedia comprising:
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(a) extracting low level features representative of the one or more concept; (b) training a discriminative classifier for each concept using a set of the low level features; (c) building a collective annotation model combining each of the discriminative classifiers; (d) defining one or more interaction potential to model interdependence between related concepts; and (e) detecting the presence/absence of one or more concepts based on the collective annotation model and the defined interaction potentials. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A system to identifying one or more concepts in digital media comprising:
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a processing component for extracting low level features representative of one or more concepts; a processing component for training a discriminative classifier for each concept using a set of the low level features; a processing component capable of building a collective annotation model based on each of the discriminative classifiers; one or more defined interaction potential to identify related concepts; and a processing component capable of identifying one or more concepts based on the collective annotation model and the defined interaction potentials.
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20. A machine readable medium having instructions stored thereon that when executed by a processor cause a system to:
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extract low level features representative of the one or more concept; train a discriminative classifier for each concept using a set of the low level features; build a collective annotation model based on each of the discriminative classifiers; define one or more interaction potential to identify related concepts, and identify one or more concepts based on the collective annotation model and the defined interaction potentials.
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Specification