Unsupervised Visual Hashing with Semantic Assistant for Content-Based Image Retrieval

Lei Zhu, Jialie Shen, Liang Xie, Zhiyong Cheng

Research output: Contribution to journalArticlepeer-review

171 Citations (Scopus)
499 Downloads (Pure)


As an emerging technology to support scalable content-based image retrieval (CBIR), hashing has recently received great attention and became a very active research domain. In this study, we propose a novel unsupervised visual hashing approach called semantic-assisted visual hashing (SAVH). Distinguished from semi-supervised and supervised visual hashing, its core idea is to effectively extract the rich semantics latently embedded in auxiliary texts of images to boost the effectiveness of visual hashing without any explicit semantic labels. To achieve the target, a unified unsupervised framework is developed to learn hash codes by simultaneously preserving visual similarities of images, integrating the semantic assistance from auxiliary texts on modeling high-order relationships of inter-images, and characterizing the correlations between images and shared topics. Our performance study on three publicly available image collections: Wiki, MIR Flickr, and NUS-WIDE indicates that SAVH can achieve superior performance over several state-of-the-art techniques.
Original languageEnglish
Pages (from-to)472-486
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number2
Publication statusPublished - 01 Feb 2017


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