Image data multi-label classification method

Abstract

The invention discloses an image data multi-label classification method. The method comprises the following steps: firstly, constructing a topological relation of pictures; then, using a multi-label propagation algorithm (CMLP) based on cooperation for solving the problem of semi-supervised multi-label learning (SSML), thereby obtaining a confidence matrix of unmarked pictures. According to the invention, the multi-label classification prediction performance of the pictures can be improved by fully utilizing the correlation among the plurality of labels of the multi-label image data, so that the accuracy and robustness are improved; the distribution information of the non-supervised image data can be mined, so that the method well solves the problem of semi-supervised multi-label learning,and is superior to the existing semi-supervised image data multi-label classification method in performance.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to a multi-label classification method for image data. Background technique [0002] Image classification is one of the most widely used machine learning problems. In real-world image classification applications, an image instance is often associated with multiple labels, and there are correlations among these labels. Therefore, classifying images with multiple related labels is an important research task. In traditional image labeling problems, a common assumption is that each image in the training dataset is accurately labeled. Unfortunately, in many real problems, although a large number of images are easy to obtain, the labels of the images need to go through an expensive and time-consuming manual labeling process. [0003] To this end, people have proposed a semi-supervised learning method, adding a large number of unlabeled images to limited labeled images to train together ...

AI Technical Summary

Problems solved by technology

But when an image instance has multiple related labels, simple label propagation cannot make good use of this correlation, resulting in a decline in classification performance.

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