Multi-label canonical correlation analysis (ml-CCA) has been developed for cross-modal retrieval. However, the computation of ml-CCA involves dense matrices eigendecomposition, which can be computationally expensive. In addition, ml-CCA only takes semantic correlation into account which ignores the cross-modal feature correlation. In this paper, we propose a novel framework to simultaneously integrate the semantic correlation and feature correlation for cross-modal retrieval. By using the semantic transformation, we show that our model can avoid computing the covariance matrix explicitly which is a huge save of computational cost. Further analysis shows that our proposed method can be solved via singular value decomposition which has linear time complexity. Experimental results on three multi-label datasets have demonstrated the accuracy and efficiency of our proposed method.

已开发出用于跨模式检索的多标签规范相关分析（ml-CCA）。但是，ml-CCA的计算涉及密集矩阵的本征分解，这可能在计算上很昂贵。此外，ml-CCA仅考虑语义相关性，而忽略了交叉模式特征相关性。在本文中，我们提出了一个新颖的框架，可以同时集成语义相关性和特征相关性以进行跨模式检索。通过使用语义转换，我们表明我们的模型可以避免显式地计算协方差矩阵，从而节省了计算成本。进一步的分析表明，我们提出的方法可以通过具有线性时间复杂度的奇异值分解来解决。