preprintOct 1, 2017Closed access

Large-Scale Image Retrieval with Attentive Deep Local Features

Pohang University of Science and Technology · Korea Post · +1 more institution

Indexed incrossref

Abstract

We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELE (DEep Local Feature). The new feature is based on convolutional neural networks, which are trained only with image-level annotations on a landmark image dataset. To identify semantically useful local features for image retrieval, we also propose an attention mechanism for key point selection, which shares most network layers with the descriptor. This frame-work can be used for image retrieval as a drop-in replacement for other keypoint detectors and descriptors, enabling more accurate feature matching and geometric verification. Our system produces reliable confidence scores to reject false…

Citation impact

847
total citations
FWCI
25.78
Percentile
100%
References
59
Citations per year

Authors

5

Topics & keywords

Keywords
  • Computer science
  • Artificial intelligence
  • Convolutional neural network
  • Pattern recognition (psychology)
  • Image retrieval
  • Clutter
  • Feature (linguistics)
  • Feature extraction
No related works found for this paper.