Concept Preserving Hashing for Semantic Image Retrieval with Concept Drift

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)5184-5197
Journal / PublicationIEEE Transactions on Cybernetics
Volume51
Issue number10
Online published16 Dec 2019
Publication statusPublished - Oct 2021

Abstract

Current hashing-based image retrieval methods mostly assume that the database of images is static. However, this assumption is not true in cases where the databases are constantly updated (e.g., on the Internet) and there exists the problem of concept drift. The online (also known as incremental) hashing methods have been proposed recently for image retrieval where the database is not static. However, they have not considered the concept drift problem. Moreover, they update hash functions dynamically by generating new hash codes for all accumulated data over time which is clearly uneconomical. In order to solve these two problems, concept preserving hashing (CPH) is proposed. In contrast to the existing methods, CPH preserves the original concept, that is, the set of hash codes representing a concept is preserved over time, by learning a new set of hash functions to yield the same set of hash codes for images (old and new) of a concept. The objective function of CPH learning consists of three components: 1) isomorphic similarity; 2) hash codes partition balancing; and 3) heterogeneous similarity fitness. The experimental results on 11 concept drift scenarios show that CPH yields better retrieval precisions than the existing methods and does not need to update hash codes of previously stored images.

Research Area(s)

  • Concept drift, concept preserving, hashing, image retrieval, nonstationary environment

Citation Format(s)

Concept Preserving Hashing for Semantic Image Retrieval with Concept Drift. / Tian, Xing; Ng, Wing W. Y.; Wang, Hui.
In: IEEE Transactions on Cybernetics, Vol. 51, No. 10, 10.2021, p. 5184-5197.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review