Method for designing manifold based regularization based semi-supervised classifier for dynamic vision

Abstract

The invention discloses a method for designing a manifold regularization based semi-supervised classifier for dynamic vision. A training data source of the classifier under the environment of the dynamic vision is as follows: data with class information acquired by a user, positive class sample including objects, negative class sample not including objects, and data without class information randomly acquired by a computer in a video; continuity regular terms of the classifier on a sample data aggregation is defined by utilizing a local linear reconstruction coefficient of the data so as to make the utilization rate of the classifier on the data without the class information be improved. In addition, the definitions of a function complexity and function continuity regular terms are configured to be a form of 1 norm in an optimization problem of solving the coefficients of the classifier, thus the solution of the optimization problem of the classifier is a sparse solution of the coefficients of the classifier, namely the classifier obtained by training is also sparse. Therefore, the real time property of the classifier in a dynamic vision task is improved.

Description

technical field [0001] The invention belongs to the field of machine vision and relates to a classifier design method for classifying dynamic visual information. Background technique [0002] With the development of pattern recognition and machine learning technology, the application of machine vision in real life is increasing. The main method is to use the camera to obtain dynamic video information, and then use the computer to simulate the human visual function, process and understand the collected visual information. Because machine vision has the characteristics of fast processing speed and large amount of information, it has a wide range of applications in identity authentication, object detection and recognition, robots, and automotive assisted driving systems. [0003] At present, dynamic vision has made great progress in the field of tracking and recognition. From the perspective of the application of machine vision as a practical application of optomechanical int...

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Problems solved by technology

[0006] The purpose of the present invention is to solve the optimization problem of solving the classifier in the prior art based on the 2-norm, which cannot guarantee the sparsity of the classifier and affects the speed of the actual application of the classifier. For this reason, the present invention provides a method for Design method of semi-supervised classifier based on manifold regularization for dynamic vision

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