Region-DH: Region-based Deep Hashing for Multi-Instance Aware Image Retrieval

Franck Romuald Fotso Mtope, Bo Wei

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Abstract

This paper introduces an instance-aware hashing approach Region-DH for large-scale multi-label image retrieval. The accurate object bounds can significantly increase the hashing performance of instance features. We design a unified deep neural network that simultaneously localizes and recognizes objects while learning the hash functions for binary codes. Region-DH focuses on recognizing objects and building compact binary codes that represent more foreground patterns. Region-DH can flexibly be used with existing deep neural networks or more complex object detectors for image hashing. Extensive experiments are performed on benchmark datasets and show the efficacy and robustness of the proposed Region-DH model.
Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks (IJCNN 2020)
Place of PublicationPiscataway
PublisherIEEE
Pages2007-2013
Number of pages7
ISBN (Electronic)9781728169262
ISBN (Print)9781728169279
DOIs
Publication statusPublished - Jul 2020
EventIJCNN 2020: International Joint Conference on Neural Networks -
Duration: 19 Jul 2020 → …

Publication series

NameNeural Networks (IJCNN)
PublisherIEEE
ISSN (Print)2161-4393

Conference

ConferenceIJCNN 2020: International Joint Conference on Neural Networks
Period19/07/20 → …

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