Scalable multi-label canonical correlation analysis for cross-modal retrieval
Shu, Xin; Zhao, Guoying (2021-02-20)
Xin Shu, Guoying Zhao, Scalable multi-label canonical correlation analysis for cross-modal retrieval, Pattern Recognition, Volume 115, 2021, 107905, ISSN 0031-3203, https://doi.org/10.1016/j.patcog.2021.107905
© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license by http://creativecommons.org/licenses/by-nc-nd/4.0/.
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe2022012811209
Tiivistelmä
Abstract
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.
Kokoelmat
- Avoin saatavuus [31995]