Supervised Fractional-Order Embedding Multiview Canonical Correlation Analysis via Ordinal Label Dequantization for Image Interest Estimation

Abstract

Supervised fractional-order embedding multiview canonical correlation analysis via ordinal label dequantization (SFEMCCA-OLD) for image interest estimation is presented in this paper. SFEMCCA-OLD is a CCA method that realizes accurate integration of features including low-dimensional ordinal label features. In general, since information is lost due to a limitation of the number of classes, i.e., the dimension of ordinal label information is smaller than those of other features, derivation of highly accurate integration of features is difficult. In SFEMCCA-OLD, the dimension of the ordinal label information can be increased by estimation of the canonical correlation between multiview features. We call this approach ordinal label dequantization. In addition, by introducing a fractional-order technique, our method can calculate optimal projections for noisy data such as real data. Experimental results show that the accuracy of SFEMCCA-OLD for image interest estimation is better than that of recent CCA-based methods.

Keywords

• Canonical correlation analysis
• ordinal label dequantization
• multiview approach
• fractional-order technique
• interest level estimation