Image sorting method and system for active learning

Abstract

The invention discloses an image sorting method and system for active learning. The image sorting method comprises the following steps: for an original unmarked image sample set, firstly considering the uncertainty of all samples only, and acquiring all image samples with teh relatively high uncertainty from the original unmarked image sample set to form a most uncertain image sample set; then evaluating the representativeness of all the samples in the most uncertain image sample set, acquiring all the samples with the relatively high representativeness and forming a most representative image sample set; subsequently, carrying out marking and sorter training on the selected samples with the relatively high uncertainty and representativeness, and sorting target images by utilizing the trained sorter. Therefore, the image sorting method and system disclosed by the invention have the advantages that the mode of layering evaluation is adopted, firstly the samples are reduced and screened on the basis of uncertainty, then the representativeness evaluation is carried out on the uncertain image sample set with the relatively high uncertainty and sample-scale reduction, so that the uncertainty and the representativeness of the samples are guaranteed, the processing time and the workload for sampling are reduced and the processing efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of image classification in pattern recognition and machine learning, and in particular relates to an active learning image classification method and system. Background technique [0002] Image classification is an image processing method that distinguishes different types of objects according to different features reflected in image information, which is a very important research topic in the field of image processing. The key problem of image classification is to train a classifier model with high classification accuracy based on the labeled pictures. [0003] Due to the high cost of image labeling (manual labeling by domain experts is required), this field provides an image classification method based on active learning to reduce the workload of manual labeling by domain experts. This method selects as few samples as possible but with high information content for category labeling, and trains a classifier...

AI Technical Summary

Problems solved by technology

However, when the above method collects image samples, the uncertainty and representativeness of the samples are comprehensively considered, and the image sample with a larger combined value of the two is selected as a sample with higher information content. , for all samples in the original unlabeled sample set, it is necessary to calculate and measure the uncertainty and representativeness of each sample, resulting in a large sampling workload, time-consuming and laborious, especially when the unlabeled sample set is large, this shortcoming more prominent

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