Image classification method based on active semi-supervised learning

Abstract

The invention relates to the technical field of image processing, and provides an image classification method based on active semi-supervised learning, comprising the following steps: randomly selecting part of labeled samples and all unlabeled samples for training semi-supervised dictionary learning components in a model; The criterion based on predicting the probability of classification iteratively selects the unlabeled samples which contain the most information from the unlabeled dataset, namely the most detailed samples. A user is introduced to tag the most informative samples, and then the most informative samples that have completed the tagging are added to the tagged dataset for training the active learning components in the model. steps are repeated to iteratively update the modeluntil the algorithm finally converges or reaches a certain number of iterations; The model is used to classify the images of the test samples. The invention solves the problem of poor expression ability between classes, combines semi-supervised learning and active learning, effectively utilizes all training data, and improves the performance of the algorithm model.

Description

technical field [0001] The present invention relates to the technical field of image processing, and more specifically, to an image classification method based on active semi-supervised learning. Background technique [0002] With the development of mobile phones, cameras and social networks, a large number of photos are rapidly created by users, and in order to be able to utilize these photos, we need an automated taxonomy that collects, categorizes and organizes them in an easy, fast and effective way The basis of automatic classification technology is to be able to train a robust machine learning classification model through data. However, collecting labeled data for training classification models is one of the most time- and labor-intensive tasks in machine learning. In real-world environments, usually labeled training samples are very limited, while it is relatively easy to obtain abundant unlabeled training samples. [0003] Semi-supervised learning and active learni...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome at least one defect in the above-mentioned prior art that unlabeled data cannot be directly used and depends on the performance of the initial classifier, the present invention provides an image cla...

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