Application of image recognition in civil aviation security based on tensor learning

Abstract

Image recognition is a hot topic in the field of computer vision and pattern recognition, it is widely used in identification, automatic control, human-computer interaction systems. With the development of civil aviation, image recognition has become an important tool to ensure civil aviation secur ity. In this article, firstly, tensor is used to represent the image, which can preserve more structure information of image than traditional vector representation. Then, combining a new tensor distance (NTD) and multilinear discriminant subspace analysis (MLDSA), a novel dimensionality reduction approach named NTD-MLDSA is proposed, and the transformation matrices can be obtained by employing an iterative strategy. Different from the Euclidean distance (ED), which bases on orthogonal assumption, NTD takes into account the spatial relationships of elements and can reflect the real distance between tensors. Experimental results show that the propose approach is more appropriate for dimensionality reduction of image objects than other classical dimension reduction methods, based on benchmark recognition databases Yale, ORL and USPS, the low dimensional data obtained by NTD-MLDSA improves the classification accuracy.