Image recognition method and device based on non-negative low-rank representation and semi-supervised learning

Abstract

The invention provides an image recognition method and device based on non-negative low-rank representation and semi-supervised learning. The method includes the following steps that: an image data set is obtained, wherein the data set contains marked data and unmarked data; an objective function is obtained according to a Gaussian field, a harmonic function and a low-rank representation function,non-negative constraint is performed on the coefficient of the low-rank representation function, the objective function is converted into a Lagrangian function, and variables, Lagrangian multipliersand a penalty factor in the Lagrangian function are updated; and iterative updating is carried out continuously until the method terminates, and the label matrix of the image data set is outputted, and test data are classified and identified according to the label matrix. According to the image recognition method and device of the invention, the semi-supervised learning and the low-rank representation are combined, and therefore, global structure information and local structure information can be well utilized, and the corruption of samples can be effectively eliminated or mitigated. The method and device have high robustness to noises and can obtain high classification performance regardless of whether training samples or test samples are damaged.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image recognition method and device based on non-negative low-rank and semi-supervised learning. Background technique [0002] Biometric technology is still one of the research hotspots in computer vision and artificial intelligence. Because face recognition is simple and contactless, it has been extensively studied over the past few decades. However, due to its high dimensionality, face recognition is still a difficult problem. It can be seen that when dealing with high-dimensional data, the consumption of time and memory is not allowed, and these data are difficult to process through some existing algorithms. Dimensionality reduction can obtain efficient low-dimensional representations of high-dimensional data, which facilitates computation, classification, storage, and visualization. Therefore, many dimensionality reduction algorithms have been proposed. The mo...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide an image recognition method and device based on non-negative low-rank and semi-supervised learning, which is used to solve the problem that the image recognition method in the prior art does not consider the global structure information and local structure information of the image at the same time

Images