Portrait data classification method based on support vector machine

Abstract

The invention relates to the technical field of portrait data classification, in particular to a portrait data classification method based on a support vector machine, which comprises the following steps: acquiring original portrait data, and removing noise in the original portrait data by adopting a denoising method based on complete random forest (CRF) to obtain a low-noise portrait data set; and inputting the low-noise portrait data set as a training set of a support vector machine into an SVM, classifying the low-noise portrait data by the SVM to obtain classified portrait data, and taking the classified portrait data as a new training set for portrait recognition. According to the method, similar noise in the portrait data can be effectively removed, the problem that classification is difficult when a support vector machine SVM faces a training set containing more noise is solved, the classification efficiency and precision of the portrait data are improved, the new data set after classification is used for the training set for portrait recognition, and the success rate of portrait recognition is indirectly improved.

Description

technical field [0001] The invention relates to the technical field of portrait data classification, in particular to a classification method of portrait data based on a support vector machine. Background technique [0002] Support vector machine (Support Vector Machine, SVM) is a kind of generalized linear classifier for binary classification of data according to supervised learning. The idea of ​​support vector machine is mainly applied to the method of solving multi-category problems, mainly including one kind of residual class, pairwise classification and decision tree method, etc., and it is mainly used in pattern recognition, such as the recognition of handwritten numbers, speech recognition, etc. , and was later applied to various fields such as bioinformatics, face detection, and text classification networks, and achieved good results. [0003] Face recognition technology is a technology that uses computers to analyze face images and extract effective identification...

AI Technical Summary

Problems solved by technology

[0005] 1. It is difficult to implement large-scale training samples: the space consumption of SVM is mainly to store training samples and kernel matrices, because SVM uses quadratic programming to solve support vectors, and solving quadratic programming will involve the calculation of m-order matrices (m is The number of samples), when the number of m is large, the storage and calculation of the matrix will consume a lot of machine memory and computing time

If the amount of data is large, the training time of SVM will be longer

[0006] 2. Difficulties in solving multi-category problems: the classic support vector machine algorithm only gives the algorithm of two-class classification, but in practical applications, it is generally necessary to solve multi-class classification problems

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