Weakly-paired Cross-modal Hashing

Abstract

Hashing has been widely adopted for large-scale data retrieval in many domains, due to its low storage cost and high retrieval speed. Existing cross-modal hashing methods optimistically assume that the correspondence between training samples across modalities are readily available. This assumption is unrealistic in practical applications. In addition, these methods generally require the same number of samples across different modalities, which restricts their flexibility. We propose a flexible cross-modal hashing approach (Flex-CMH) to learn effective hashing codes from weakly-paired data, whose correspondence across modalities are partially (or even totally) unknown. FlexCMH first introduces a clustering-based matching strategy to explore the local structure of each cluster, and thus to find the potential correspondence between clusters (and samples therein) across modalities. To reduce the impact of an incomplete correspondence, it jointly optimizes in a unified objective function th

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• arxiv keyliu2019weakly

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