Image labeling method, system, and device and computer-readable storage medium

Abstract

The embodiment of the invention discloses an image labeling method, system, and device and a computer-readable storage medium. The method comprises: a training sample set and a to-be-labeled image into a convolutional neural network, wherein the training sample set employs a back propagation algorithm to minimize a cross entropy loss function in the convolutional neural network to adjust the weight of the convolutional neural network for training and the weight of the trained convolutional neural network is loaded again to extract a sample network feature set of the training sample set and a test network feature set of the to-be-labeled image; according to the sample network feature set, the test network feature set, and a label set, the probability of belong to each kind of label in the to-be-labeled image is calculated and thus a label probability set is generated; and according to the label probability set, labeling is carried out on the to-be-labeled image. On the basis of combination of the deep learning and label propagation algorithms, the high-level semantic features of images are extracted automatically, so that the efficiency and accuracy of image labeling are improved.

Description

technical field [0001] The embodiments of the present invention relate to the field, and in particular, relate to an image labeling method, system, device, and computer-readable storage medium. Background technique [0002] With the rapid development of computer vision technology, image processing technology has also been developed accordingly. Image annotation is an important and challenging topic in the field of computer vision. Image annotation is a technology that allows computers to automatically add semantic keywords that can reflect image content to unlabeled images. Image annotation uses labeled image sets or other available information to automatically learn the relationship model between semantic concept space and visual feature space, and use this model to label images with unknown semantics. [0003] Image annotation attempts to establish a mapping relationship between the high-level semantic information and low-level features of the image, so it can solve the "...

AI Technical Summary

Problems solved by technology

However, using the above image annotation method requires a lot of time to manually select features, which is time-consuming and labor-intensive, and will ignore semantic neighbors, resulting in visually similar but semantically dissimilar situations, affecting image annotation effects

Images