期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 35, 期 2, 页码 1391-1401出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2021.3107489
关键词
Semantics; Training data; Training; Task analysis; Media; Data models; Videos; Hashing; matrix factorization; cross-media retrieval; consistency and inconsistency; discrete optimization
Cross-media hashing encodes data points from different modalities into a common Hamming space, and has been successfully applied to large-scale multimedia retrieval. However, existing methods neglect the potential inconsistency among different modalities, which may undermine retrieval accuracy. To address this problem, we propose a novel unsupervised hashing model, DRMFH, which formulates the consistency and inconsistency across different modalities into a matrix factorization based model.
Cross-media hashing, which encodes data points from different modalities into a common Hamming space, has been successfully applied to solve large-scale multimedia retrieval issue due to storage efficiency and search effectiveness. Recently, matrix factorization based hashing methods have drawn considerable attention for their promising search accuracy. However, pioneer methods mainly focus on learning consensus hash codes for different modalities, but neglect the potential inconsistency among different modalities, e.g., the diversities of different modalities and noises, which may undermine the retrieval accuracy. To address this problem, we propose a novel unsupervised hashing model, namely, Discrete Robust Matrix Factorization Hashing (DRMFH), which simultaneously formulates the consistency and inconsistency across different modalities into a matrix factorization based model. Specifically, a homogenous space composed of a consistent Hamming space and an inconsistent diversity part, are generated by matrix factorization for each modality. Therefore, the consensus information across different modalities can be well captured in the learnt hash codes, leading to improved retrieval performance. Moreover, we design an effective optimization algorithm which is able to obtain an approximate discrete code matrix with linear time complexity. Comprehensive experimental results on three public multimedia retrieval datasets show that the proposed DRMFH outperforms several state-of-the-art methods.
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