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The new feature is\nbased on convolutional neural networks, which are trained only with image-level\nannotations on a landmark image dataset. To identify semantically useful local\nfeatures for image retrieval, we also propose an attention mechanism for\nkeypoint selection, which shares most network layers with the descriptor. This\nframework can be used for image retrieval as a drop-in replacement for other\nkeypoint detectors and descriptors, enabling more accurate feature matching and\ngeometric verification. Our system produces reliable confidence scores to\nreject false positives---in particular, it is robust against queries that have\nno correct match in the database. To evaluate the proposed descriptor, we\nintroduce a new large-scale dataset, referred to as Google-Landmarks dataset,\nwhich involves challenges in both database and query such as background\nclutter, partial occlusion, multiple landmarks, objects in variable scales,\netc. We show that DELF outperforms the state-of-the-art global and local\ndescriptors in the large-scale setting by significant margins. Code and dataset\ncan be found at the project webpage:\nhttps://github.com/tensorflow/models/tree/master/research/delf .","url_abs":"http://arxiv.org/abs/1612.06321v4","url_pdf":"http://arxiv.org/pdf/1612.06321v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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