This paper presents supervised multiview canonical correlation analysis via ordinal label dequantization (sMVCCA-OLD) for image interest estimation.
sMVCCA-OLD is a new supervised CCA method realizing accurate integration of features including low-dimensional ordinal label features by introducing label dequantization scheme to sMVCCA.
In sMVCCA, there is a possibility of missing information that is necessary for image interest estimation since the dimensions of integrated features is limited by the number of classes.
sMVCCA-OLD can solve this problem by increasing the dimension of the ordinal label information with the estimation of the canonical correlation between multiview features.
From experimental results obtained by applying our method to the image interest level estimation, it is confirmed that accuracy improvement using sMVCCA-OLD becomes feasible compared to recent CCA-based methods.