Unsupervised Deep Cross-modality Spectral Hashing

Tuan Hoang, Thanh-Toan Do, Tam V. Nguyen, Ngai Man Cheung

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This paper presents a novel framework, namely Deep Cross-modality Spectral Hashing (DCSH), to tackle the unsupervised learning problem of binary hash codes for efficient cross-modal retrieval. The framework is a two-step hashing approach which decouples the optimization into (1) binary optimization and (2) hashing function learning. In the first step, we propose a novel spectral embedding-based algorithm to simultaneously learn single-modality and binary cross-modality representations. While the former is capable of well preserving the local structure of each modality, the latter reveals the hidden patterns from all modalities. In the second step, to learn mapping functions from informative data inputs (images and word embeddings) to binary codes obtained from the first step, we leverage the powerful CNN for images and propose a CNN-based deep architecture to learn text modality. Quantitative evaluations on three standard benchmark datasets demonstrate that the proposed DCSH method consistently outperforms other state-of-the-art methods.

Original languageEnglish
Pages (from-to)8391-8406
Number of pages16
JournalIEEE Transactions on Image Processing
Volume29
DOIs
Publication statusPublished - 12 Aug 2020
Externally publishedYes

Keywords

  • constraint optimization
  • Cross-modal retrieval
  • image search
  • spectral hashing

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