Robust visual image classification method and system

Abstract

The present invention discloses a robust visual classification method and system and aims to effectively achieve category prediction of a no-label sample in a training sample and rapid induction and reasonable dimension reduction of a to-be-detected sample. The method comprises: integrating an error metric based on elastic regression analysis into a label propagation model outside the training sample; by a parameter, weighing the influence of a normalization manifold regularization term, a label fitting term based on soft label l2, 1 norm regularization and an elastic regression residual term based on l2, 1 norm regularization on sample description and category identification so as to complete establishing a label propagation model; and then, iteratively optimizing the label propagation model to acquire a projection matrix for determining a category of a to-be-detected sample. Therefore, according to the robust visual classification method and system, by introducing a regression error term based on l2, 1 norm regularization and soft label l2, 1 norm regularization, robustness of the system can be effectively improved while the advantages of a label propagation classification method is carried on, so that the induction process of the to-be-detected sample is rapid and accurate.

Description

technical field [0001] The present invention relates to the technical field of data mining, and more specifically, relates to a robust visual image classification method and system. Background technique [0002] Classification technology has always been the most fundamental and core research topic in data mining. In recent years, classification technology has developed rapidly, and many excellent classification methods have emerged, such as SVM, decision tree, association rules, neural network and deep learning, etc. Many classification systems have been put into use and have produced huge social and economic benefits. In the field of label propagation, a projection-based fast inductive classification method has emerged, which greatly improves the accuracy and speed of label propagation classification. [0003] Label propagation classification is a semi-supervised learning method based on similarity graph construction. In actual operation, the supervised data (data with c...

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Problems solved by technology

[0005] The purpose of the present invention is to provide a robust visual image classification method and system to solve the problem of slow classification speed in the label propagation classification method of the prior art

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