Deep Region Hashing for generic instance search from images

Jingkuan Song, Tao He, Lianli Gao, Xing Xu, Heng Tao Shen

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

14 Citations (Scopus)

Abstract

Instance Search (INS) is a fundamental problem for many applications, while it is more challenging comparing to traditional image search since the relevancy is defined at the instance level. Existing works have demonstrated the success of many complex ensemble systems that are typically conducted by firstly generating object proposals, and then extracting handcrafted and/or CNN features of each proposal for matching. However, object bounding box proposals and feature extraction are often conducted in two separated steps, thus the effectiveness of these methods collapses. Also, due to the large amount of generated proposals, matching speed becomes the bottleneck that limits its application to large-scale datasets. To tackle these issues, in this paper we propose an effective and efficient Deep Region Hashing (DRH) approach for large-scale INS using an image patch as the query. Specifically, DRH is an end-to-end deep neural network which consists of object proposal, feature extraction, and hash code generation. DRH shares full-image convolutional feature map with the region proposal network, thus enabling nearly cost-free region proposals. Also, each high-dimensional, real-valued region features are mapped onto a low-dimensional, compact binary codes for the efficient object region level matching on large-scale dataset. Experimental results on four datasets show that our DRH can achieve even better performance than the state-of-the-arts in terms of mAP, while the efficiency is improved by nearly 100 times.

Original languageEnglish
Title of host publicationThe Thirty-Second AAAI Conference on Artificial Intelligence
EditorsSheila McIlraith, Kilian Weinberger
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages402-409
Number of pages8
ISBN (Electronic)9781577358008
DOIs
Publication statusPublished - 2018
Externally publishedYes
EventAAAI Conference on Artificial Intelligence 2018 - New Orleans, United States of America
Duration: 2 Feb 20187 Feb 2018
Conference number: 32nd
https://aaai.org/Conferences/AAAI-18/

Conference

ConferenceAAAI Conference on Artificial Intelligence 2018
Abbreviated titleAAAI 2018
Country/TerritoryUnited States of America
CityNew Orleans
Period2/02/187/02/18
Internet address

Keywords

  • hashing
  • region
  • image retrieval

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