Scalable multi-label canonical correlation analysis for cross-modal retrieval
作者：
Highlights：
• We develop a novel framework that can integrate the feature correlation and semantic correlation to boost retrieval performance.
• A semantic transformation is developed to effectively approximate the label similarity matrix.
• We propose an efficient learning algorithm which has linear time complexity in terms of the number of samples.
摘要
•We develop a novel framework that can integrate the feature correlation and semantic correlation to boost retrieval performance.•A semantic transformation is developed to effectively approximate the label similarity matrix.•We propose an efficient learning algorithm which has linear time complexity in terms of the number of samples.
论文关键词：Canonical correlation analysis,Semantic transformation,Cross-modal retrieval,Singular value decomposition
论文评审过程：Received 8 July 2020, Revised 18 January 2021, Accepted 17 February 2021, Available online 20 February 2021, Version of Record 28 February 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.107905
