Complementary Incremental Hashing with Query-adaptive Re-ranking for Image Retrieval

Xing TIAN, Wing W. Y. NG, Hui WANG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

15 Citations (Scopus)

Abstract

Concept drift is prevalent in non-stationary data environments but is rarely researched in image retrieval. Therefore, more research is needed on image retrieval in non-stationary data environments so that highly relevant images can still be retrieved when concept drifts happen. Hashing is a key technique to allow efficient image retrieval, so incremental hashing technique emerges in recent years for image retrieval in non-stationary environments. A state-of-the-art method is Incremental Hashing (ICH). ICH trains new hash tables on new data without considering the performance of previous hash tables, so the dependency of successive hash tables is ignored. To make use of this dependency in order to improve the performance of image retrieval in non-stationary environments, Complementary Incremental Hashing with query-adaptive Re-ranking (CIHR) is proposed in this paper. CIHR trains multiple hash tables incrementally, one for each data chunk of images. A new hash table is trained on a new data chunk of images as well as those images badly hashed by previous hash tables, thus the new hash table is complementary to the previous hash tables. To use the hash tables more effectively, a query-adaptive re-ranking method is used to weight all hash functions in each hash table according to their retrieval performance with respect to a given query. Weighted Hamming distance is finally used to evaluate the similarity between the query and the images in the database, as the basis of image retrieval. Experimental results on simulated non-stationary scenarios show that the proposed CIHR method achieves higher retrieval accuracy than all methods being compared, thus setting a new state of the art in image retrieval in non-stationary data environments.

Original languageEnglish
Pages (from-to)1210-1224
Number of pages15
JournalIEEE Transactions on Multimedia
Volume23
Early online date14 May 2020
DOIs
Publication statusPublished - 2021
Externally publishedYes

Bibliographical note

The associate editor coordinating the review of this manuscript and approving it for publication was Dr. MengWang.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 61876066, 61772344, and 61672443, in part by Guangzhou Science and Technology Plan Project underGrant 201804010245, in part by Guangdong Province Science and Technology Plan Project (Collaborative Innovation and Platform Environment Construction) under Grant 2019A050510006, in part by Hong Kong RGC General Research Funds under Grants 9042489 (CityU 11206317), 9042816 (CityU 11209819), and 9042322 (CityU 11200116), and in part by EU Horizon 2020 Programme (700381, ASGARD).

Keywords

  • Concept Drift
  • Hashing
  • Image Retrieval
  • Non-stationary Environment
  • Re-ranking

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