Due to its fast computational speed and low storage cost, hashing has been effectively applied to large-scale multimedia retrieval tasks, such as medical video and security video retrieval. Most existing cross-view hashing methods require good matching information, however, this exact pairing relationship is difficult to fully realise in practice. The association between views is incomplete, as is the label information. This task of missing paired and labelled information is very challenging, but less explored in research. In this study, a semi-supervised semi-paired deep hashing for large-scale data is proposed, named Semi-Paired Semi-Supervised Deep Hashing (SPSDH) to solve this challenging task. SPSDH is a novel end-to-end deep neural network model with high-order affinity. A non-local higher-order affinity measure that better considers the multimodal neighbourhood structure is proposed. A common representation to associate different modalities is introduced, which combined with the labelled information greatly maintains the consistency within the modalities. SPSDH is evaluated on three benchmark datasets for large-scale cross-view approximate nearest neighbour search and compared with several state-of-the-art hashing methods. Extensive experimental results demonstrate the superior performance of our proposed SPSDH in semi-supervised semi-paired retrieval tasks. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/cvi2.12153

Semi‐Paired Semi‐Supervised Deep Hashing for cross‐view retrieval

Global centralised and structured discriminative non‐negative matrix factorisation for hyperspectral unmixing

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Semi‐Paired Semi‐Supervised Deep Hashing for cross‐view retrieval

Global centralised and structured discriminative non‐negative matrix factorisation for hyperspectral unmixing