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Searching and annotating 100M images with YFCC100M-HNfc6 and MI-File

Contributo in Atti di convegno
Data di Pubblicazione:
2017
Abstract:
We present an image search engine that allows searching by similarity about 100M images included in the YFCC100M dataset, and annotate query images. Image similarity search is performed using YFCC100M-HNfc6, the set of deep features we extracted from the YFCC100M dataset, which was indexed using the MI-File index for efficient similarity searching. A metadata cleaning algorithm, that uses visual and textual analysis, was used to select from the YFCC100M dataset a relevant subset of images and associated annotations, to create a training set to perform automatic textual annotation of submitted queries. The on-line image and annotation system demonstrates the effectiveness of the deep features for assessing conceptual similarity among images, the effectiveness of the metadata cleaning algorithm, to identify a relevant training set for annotation, and the efficiency and accuracy of the MI-File similarity index techniques, to search and annotate using a dataset of 100M images, with very limited computing resources.
Tipologia CRIS:
04.01 Contributo in Atti di convegno
Keywords:
Deep Learning; Image Annotation; Content-Based Image Retrieval
Elenco autori:
Amato, Giuseppe; Gennaro, Claudio; Falchi, Fabrizio; Rabitti, Fausto
Autori di Ateneo:
AMATO GIUSEPPE
FALCHI FABRIZIO
GENNARO CLAUDIO
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/344735
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URL

https://dl.acm.org/citation.cfm?doid=3095713.3095740
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