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

Amato G., Falchi F., Gennaro C., Rabitti F.

Content-Based Image Retrieval  Image Annotation  Deep Learning 

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.

Source: CBMI '17 - 15th International Workshop on Content-Based Multimedia Indexing, Firenze, Italy, 19-21 June 2017

Publisher: ACM Press, New York, USA

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