Hashing methods have sparked a great revolution on large-scale cross-media search due to its effectiveness and efficiency. Most existing approaches learn unified hash representation in a common Hamming space to represent all multimodal data. However, the unified hash codes may not characterize the cross-modal data discriminatively, because the data may vary greatly due to its different dimensionalities, physical properties, and statistical information. In addition, most existing supervised cross-modal algorithms preserve the similarity relationship by constructing an n × n pairwise similarity matrix, which requires a large amount of calculation and loses the category information. To mitigate these issues, a novel cross-media hashing approach is proposed in this article, dubbed label flexible matrix factorization hashing (LFMH). Specifically, LFMH jointly learns the modality-specific latent subspace with similar semantic by the flexible matrix factorization. In addition, LFMH guides the hash learning by utilizing the semantic labels directly instead of the large n × n pairwise similarity matrix. LFMH transforms the heterogeneous data into modality-specific latent semantic representation. Therefore, we can obtain the hash codes by quantifying the representations, and the learned hash codes are consistent with the supervised labels of multimodal data. Then, we can obtain the similar binary codes of the corresponding modality, and the binary codes can characterize such samples flexibly. Accordingly, the derived hash codes have more discriminative power for single-modal and cross-modal retrieval tasks. Extensive experiments on eight different databases demonstrate that our model outperforms some competitive approaches.

中文翻译：

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用于高效跨模态检索的标签一致的灵活矩阵分解散列

哈希方法因其有效性和效率而引发了大规模跨媒体搜索的巨大革命。大多数现有方法在公共汉明空间中学习统一哈希表示来表示所有多模态数据。然而，统一的哈希码可能无法区分地表征跨模态数据，因为数据可能由于其不同的维度、物理特性和统计信息而有很大差异。此外，大多数现有的有监督跨模态算法通过构造一个n×n成对相似度矩阵，计算量大，丢失类别信息。为了缓解这些问题，本文提出了一种新的跨媒体散列方法，称为标签灵活矩阵分解散列（LFMH）。具体来说，LFMH 通过灵活的矩阵分解联合学习具有相似语义的特定模态潜在子空间。此外，LFMH 通过直接利用语义标签而不是大n×n成对相似矩阵。LFMH 将异构数据转换为特定于模态的潜在语义表示。因此，我们可以通过量化表示获得哈希码，并且学习的哈希码与多模态数据的监督标签一致。然后，我们可以得到相应模态的相似二进制码，二进制码可以灵活地表征这样的样本。因此，派生的哈希码对于单模态和跨模态检索任务具有更大的辨别力。对八个不同数据库的大量实验表明，我们的模型优于一些竞争方法。