Multi-feature Fusion Based on Supervised Multi-view Multi-label Canonical Correlation Projection

Abstract

This paper presents multi-feature fusion based on supervised multi-view multi-label canonical correlation projection (sM2CP). The proposed method applies sM2CP-based feature fusion to multiple features obtained from various convolutional neural networks (CNNs) whose characteristics are different. Since new fused features with high representation ability can be obtained, performance improvement of multi-label classification is realized. Specifically, in order to tackle the multi-label problem, sM2CP introduces a label similarity information of label vectors into the objective function of supervised multi-view canonical correlation analysis. Thus, sM2CP can deal with complex label information such as multi-label annotation. The main contribution of this paper is the realization of feature fusion of multiple CNN features for the multi-label problem by introducing multi-label similarity information into the canonical correlation analysis-based feature fusion approach. Experimental results show the effectiveness of sM2CP, which enables effective fusion of multiple CNN features.