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2018 | OriginalPaper | Buchkapitel

Analysis of Neural Codes for Near-Duplicate Detection

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Abstract

An important feature of digital asset management platforms and search engines is the possibility of retrieving near-duplicates of an image given by the user. Near-duplicates could be photos derived from an original photo after a certain transformation or different photos of the same scene. In this work we analyze the two cases, using convolutional neural networks for calculating the signatures of the images, introducing a new training set for model creation and some new datasets for performance evaluation. Results on these datasets and in standard datasets for image retrieval will be presented and discussed.

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Metadaten
Titel
Analysis of Neural Codes for Near-Duplicate Detection
verfasst von
Maurizio Pintus
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-01449-0_30