Two-directional two-dimensional fractional-order embedding canonical correlation analysis for multi-view dimensionality reduction and set-based video recognition

Set-based video recognition is an important application in practice, and many specialized approaches have been proposed. However, most of these methods either only use one kind of visual features for classification, or are sensitive to the noises and the number of training images when using several kinds of features, resulting in limited discrimination. To explore a possible solution to these issues, in this paper, a novel efficiently and effectively dimensionality reduction method, named two-directional two-dimensional fractional-order embedding canonical correlation analysis ((2D)FECCA), is proposed. (2D)FECCA borrows the idea of fractional-order embedding to correct the estimation of sample covariance matrices, which can significantly reduce the influence of noise disturbance and effectively utilizing the discriminative information from different view features. In addition, several set-based video recognition schemes are introduced to determine the labels of the test videos. Extensive experimental results on four familiar single image based databases and two video based benchmark databases demonstrate the effectiveness of the proposed method. These quantitative assessments reinforce the significance as well as the importance of embedding the proposed method in other intelligent systems application areas.