Graph learning model based on reconstructed graph

Abstract

The invention discloses a graph learning model based on a reconstructed graph, and belongs to the field of image annotation, and the method comprises the following steps: searching a semantic nearestneighbor of a test image through an improved nearest neighbor algorithm, constructing a similar matrix, carrying out the clustering of the image through a random dot product graph, mining the internalconnection of the image, obtaining a weighted similar matrix, and obtaining a preliminary image annotation result through a graph learning algorithm. The relation between the labels is used for labeling, the co-occurrence imbalance between the labels is considered in the process, a nearest graph theory model is introduced, and the problem of label imbalance is effectively solved. The random dot product image is used for reconstructing a transfer matrix of labels, and the problem of image label coexistence asymmetry is solved. Further, a Naive Bayes nearest neighbor classifier is used to establish a joint likelihood function between the image and the tag. The image labeling model based on the reconstructed image model is provided according to the characteristic of unbalanced classificationof the image labels, and the recall rate of the labels can be effectively increased.

Description

technical field [0001] The invention belongs to the technical field of image labeling, and relates to a graph learning model based on reconstructed graphs, which is used to deal with the problem of image labeling under the background of big data. Background technique [0002] The advent of the big data era has brought many opportunities, but also brought more challenges. With the popularization of smart devices and the development of mobile networks, people are more and more fond of sharing images on the Internet, which produces a large number of images, which contain huge wealth. Therefore, efficient image analysis has become of great research significance hotspots. However, there are a large number of unlabeled images in these massive images, which poses a huge challenge to effectively mine the value of images. The graph learning model is a typical image annotation method, which is based on unsupervised, semi-supervised, and supervised learning strategies, and shares lab...

AI Technical Summary

Problems solved by technology

[0006] (2) Label co-occurrence imbalance phenomenon: In the image training set, there will be a phenomenon of simultaneous occurrence between labels

[0008] (1) The problem of low label recall rate: This problem refers to that when the image is labeled, the number of labels for some labels itself is too small, which will greatly reduce the probability of being selected during automatic labeling

[0009] (2) Label co-occurrence imbalance problem: This problem refers to the probability of co-occurrence between labels, but the co-occurrence probability between them is not exactly the same

[0010] (3) Visual ambiguity problem: This problem refers to the large difference between images with the same label, resulting in too large visual distance between them, which reduces the possibility of the image being selected as a semantic neighbor and affects the accuracy of image annotation

Images