Data-driven iterative image online annotation method

Abstract

The invention discloses a data-driven iterative image online annotation method. The method comprises the following steps: 1, data preparation and recovery: making a user feed samples to be annotated into a sample buffer pool successively, performing scale detection on the sample buffer pool, performing feature extraction, and feeding the samples to be annotated into a circular annotation module; and 2, circular annotation: at the start of each round of circulation process, calculating a similarity matrix of features through an online similarity measurement model, optimizing the similarity matrix through constraint propagation, performing data clustering on the similarity matrix by a spectral clustering algorithm, selecting a significant category, pushing the significant category to the user for annotation, cycling constantly till remaining samples are not enough to continue annotation, and exiting the circulation process to obtain a personalized label of an input image region given by the user. Through the technology of the method, rapid and accurate annotation of images can be realized; the annotation efficiency is increased; and the method can adapt to a personalized annotation system of the user.

Description

technical field [0001] The invention relates to an image labeling method, which belongs to the technical field of image processing, in particular to a data-driven iterative image online labeling method. Background technique [0002] In recent years, with the rapid development of Internet technology, multimedia images, and storage devices, the number of images that users come into contact with has grown explosively. How to quickly and effectively classify and organize a large amount of user image data to meet other application needs of users is an important research direction. Traditional image annotation aims at classification and retrieval, and is concerned with information such as scenes, objects, and relationships between objects in the image as a whole or in parts. Therefore, in the context of increasing user image data, image annotation should be more flexible, with a variety of annotation systems to adapt to different applications of different users, while maintaining...

AI Technical Summary

Problems solved by technology

[0005] Based on the above image labeling methods, the model-driven labeling method has shortcomings such as relying on hypothetical models, relying on a large amount of manual labeling training data, and lacking flexibility in the labeling system, making it unsuitable for large-scale user image labeling work; the existing data The driving method also has the problem of dependence on the prior knowledge of the labeled data, and there is no optimization of the feature representation for the image, and the representation ability of the image is poor.

Therefore, none of the above image annotation methods can fully meet the application requirements of image annotation.

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