CO-training method based on unlabeled-sample consistency determination

Abstract

The invention discloses a CO-training method based on unlabeled-sample consistency determination, relates to CO-training and multi-angle image classification, and aims to solve the problem that existing multi-angle remote sensing increases difficulty of joint analysis on ground objects of the same area, especially difficulty of change analysis of the ground objects, and enables classification accuracy of multi-angle remote sensing images to be low. A process includes: 1, carrying out initial classification: 2, selecting trusted samples of unlabeled samples in shot images; 3, obtaining a retrained classifier of a current view angle until retraining of classifiers corresponding to all view angles is completed; 4, obtaining a classification result of the current view angle until reclassification of the classifiers corresponding to all the view angles is completed; 5, repeatedly executing 2, 3 and 4 until an iteration termination condition is met, obtaining a classification result of eachview angle, carrying out voting, and using labels of highest voting rates as labels of a sample which is in the shot images in 1 and has no label. The method is used in the field of digital image processing.

Description

technical field [0001] The invention belongs to the field of digital image processing, relates to collaborative training and multi-angle image classification, and is a multi-angle learning method. Background technique [0002] Feature data obtained from different levels of the same object or obtained from different channels are generally called multi-view data. Multi-perspective learning usually needs to follow two principles: the principle of consistency and the principle of complementarity; the principle of consistency means that different perspectives of the same object are related to each other, and the principle of complementarity means that different perspectives of the same object are different and can be as complementary features. Existing multi-view learning algorithms are mainly divided into the following three categories: collaborative training, subspace learning and multi-kernel learning. Among them, the collaborative training algorithm learns two or more diffe...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the existing multi-angle remote sensing increases the difficulty of joint analysis of ground objects in the same area, especially the difficulty of analyzing the change of ground objects, which makes the classification accuracy of multi-angle remote sensing images low, and proposes a classifier-based A collaborative training method for unlabeled sample consistency determination (CO-training with Unlabeled Sample's Consistency hereinafter referred to as CO-USC)

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