Image labeling method based on multi-label learning, terminal device and storage medium

Abstract

The present invention relates to an image labeling method based on multi-label learning, a terminal device and a storage medium. The method comprises the following steps of: S100: extracting all the labels in examples, and calculating the label importance degree of each label corresponding to each example in a multi-label training set through a label propagation method; S200: performing resamplingof the multi-label training set according to the label importance degrees to obtain training subsets; and S300: calculating class attributes of the training subsets, and performing classification according to the class attributes. Aiming at the problems of ambiguity and amounts of images in the image labeling field, multi-label learning is employed to perform labeling of the images, the information of the relative label importance degrees hidden in the training samples is employed to construct more effective class attributes, the image labeling method is employed to construct more effective class attributes corresponding to different labels and construct a classification model of the labels on the class attributes so as to obtain better image classification effects.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to an image labeling method based on multi-label learning, a terminal device and a storage medium. Background technique [0002] With the development of society and the further improvement of technology, a large number of digital images are generated and disseminated every day. To provide related services on such a large-scale image data, one of the core and most difficult tasks is to let the computer understand the image. semantics, and image annotation is the key technology. Image annotation, also known as automatic image annotation, refers to the process in which the computer system automatically associates the concept tags contained in the image with it according to the visual content of the image. It is one of the important fields of content-based image retrieval. Nowadays, although the research in the field of image annotation has achieved a lot of results, w...

AI Technical Summary

Problems solved by technology

In traditional supervised learning, an example is only associated with one label, which represents its unique category. However, the uniqueness of this label cannot satisfy the problem of image labeling, because an image usually contains multiple semantic contents, so There are multiple categories of tags at the same time, for example, an image can include one or more semantic concepts, such as "people", "grass", "sky" and "sunset"; therefore, there is an urgent need for an image that can accurately label Semantic Image Annotation Method

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