Analysis of Neural Codes for Near-Duplicate Detection

摘要

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.