A new method of feature fusion and its application in image recognition

By Quansen Sun, Shenggen Zeng, Yan Liu, P. Heng, D. Xia. Pattern Recognition, 2005

504 citations recorded by the source corpus.

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A Review of Human Activity Recognition Methods (2015, methodology): “, 2004) was widely studied in the literature as an effective way for fusing data at the feature level (Sun et al., 2005; Wang et al., 2011c; Rudovic et al., 2013).”
A Feature Fusion Based Forecasting Model for Financial Time Series (2014, methodology): “In [53], a feature fusion framework is proposed, adopting the idea of CCA for pattern recognition.”
A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification (2010, methodology): “Feature fusion [16,17] can be divided into the following methods, namely, feature ranking, selection, feature extraction, and feature combination.”
Deep Feature Fusion for VHR Remote Sensing Scene Classification (2017, methodology): “As developed in [38], the combination between the transformed features is performed by concatenation or summation as follows:”
Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition (2016, methodology): “Recently, feature fusion based on Canonical Correlation Analysis (CCA) [9] has attracted the attention in the area of multimodal recognition.”
Stochastic Classifiers for Unsupervised Domain Adaptation (2020, methodology): “Testing-stage Prediction Previous methods with multiple classifiers usually resort to the feature/score fusion or voting by majority [17, 51] for a final decision.”