Unsupervised domain adaptation classification method based on inter-class matching

Abstract

The invention discloses an unsupervised domain adaptation classification method based on inter-class matching, so as to solve the technical problem that the present classification method for images between different domains is poor in classification performance. According to the technical scheme, according to samples in a source image domain and a target image domain, the maximum average difference model for classes is built. A linear projection method is used to build a feature representation model for domain migration, the source domain samples and the target domain samples are projected to the same hidden feature space. In joint consideration of the supervision information of the source domain samples and hidden low-rank structural features between samples in the target domain, a robust target domain classification model is built, and all unlabeled samples in the target domain are marked. A joint optimization model with minimization of the distribution difference of the same class between domains as a target is built, an alternating minimization optimization method is used, alternating iteration of the feature representation model and the classification model is carried out until convergence, the optimal target domain classification result is obtained finally, and the classification performance is good.

Description

Technical field [0001] The present invention relates to an image classification method between different fields, in particular to an unsupervised field adaptive classification method based on matching between classes. Background technique [0002] With the proliferation of visual data, computer vision usually faces a situation where the feature distribution of the source image field (training data) does not match the feature of the target field (test data). The domain adaptation method aims to reduce the performance loss of applying the classifier trained in the source domain to the target domain. Among them, the most challenging task is the unsupervised domain adaptation method. All samples in the target domain are sample-free. In order to effectively eliminate the distribution differences between domains, most methods mainly describe data characteristics in different domains. , Trying to minimize the difference between the global domain feature distribution. [0003] The docume...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of poor classification performance of existing image classification methods between d

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