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Da Silva et al., 2011 - Google Patents

Active learning paradigms for CBIR systems based on optimum-path forest classification

Da Silva et al., 2011

Document ID
8540348613050456174
Author
Da Silva A
Falcão A
Magalhães L
Publication year
Publication venue
Pattern Recognition

External Links

Snippet

This paper discusses methods for content-based image retrieval (CBIR) systems based on relevance feedback according to two active learning paradigms, named greedy and planned. In greedy methods, the system aims to return the most relevant images for a query …
Continue reading at www.sciencedirect.com (other versions)

Classifications

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    • G06F17/30244Information retrieval; Database structures therefor; File system structures therefor in image databases
    • G06F17/30247Information retrieval; Database structures therefor; File system structures therefor in image databases based on features automatically derived from the image data
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
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    • G06F17/30861Retrieval from the Internet, e.g. browsers
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    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
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    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
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