Multi-class image semi-supervised classifying method and system

Abstract

The invention discloses a multi-class image semi-supervised classifying method and system. The method comprises the steps that firstly, similarity learning is conducted on image samples with tags and image samples without tags in a training set, and similar neighbor images and normalized weights are constructed and used for representing sample similarities; secondly, a class tag matrix is initialized, L2,1-norm regularization is introduced to effectively reduce the influence of mixed signals in prediction tags F of flexible class tags on results, constrains which are not negative and are one in column sum are applied to F at the same time, and thus it is ensured that estimated flexible tags meet the probability definition and non-negativity; finally, parameters are used for balancing the influences of similarity measurement, initial class tags and L2,1-norm regularization on classification, semi-supervised learning modeling is completed, the maximum value of similarity probabilities is taken to be used for image class identification, and classification results are obtained. Due to the fact that the L2,1-norm regularization is introduced, the influence of the mixed signals on the classification is reduced, and thus the classification accuracy is improved. In addition, data outside the training set can be effectively classified, and the expansibility is good.

Description

technical field [0001] This application relates to the technical fields of data mining, machine learning and pattern classification, and in particular to a method and system for semi-supervised classification of multi-category images. Background technique [0002] With the advent of the era of information and data explosion, classification technology has become one of the most important research topics in the fields of data mining and pattern recognition. Classification mainly realizes the classification of unknown categories of data. It is of great significance in the fields of medical data analysis, credit grading of credit cards and image classification. Once the research is successful and put into application, it will produce huge social and economic benefits. However, most of the data in the real world (such as images in the Internet) have no class labels, and the manual calibration process of samples is very time-consuming, laborious and expensive, which makes the accu...

AI Technical Summary

Problems solved by technology

However, there are still some shortcomings in the existing methods, for example, how to effectively remove the mixed signals in the predicted "soft class label" prediction matrix and its impact on the classification results, and whether the output soft class label information always meets the definition of probability (that is, the probability sum is 1 and non-negative), etc.

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