Image content semanteme marking method

Abstract

Using techniques of image processing, machine learning, and semantic processing natural language etc, the method combines semantic labeling for visual character of image with semantic labeling text character of image to carry out semantic labeling content of image. Moreover, based on labeling characteristic of specific user, the method also supports to correct mapping rule base of label at bottom layer so that the labeled result is more accorded with labeling requirement of specific user. The method is widely applicable to each applications of need to carry out image searches. The method raises labeling precision, and expands range of application.

Description

technical field [0001] The present invention relates to an image content semantic labeling method for image labeling, in particular to using image processing technology, natural language processing technology and machine learning technology to carry out semantic labeling of image content by using attribute information such as visual features of image content and related text. Background technique [0002] With ever-improving digital image technology and the easy availability of the Internet in recent years, the popularity of digital images is growing rapidly, with more and more digital images becoming available every day. It is of great practical significance to design a method that can quickly and accurately retrieve the images that users need. There are currently two main image retrieval methods. One is image keyword-based retrieval, and the other is content-based image retrieval (CBIR). The difference between them is that the image content is marked in different ways. ...

AI Technical Summary

Problems solved by technology

However, the artificially generated image keyword tagging method has two main disadvantages: one is that manual inspection and careful labeling of each image are required, these steps require a lot of labor and the cost is very high, especially with the popularity of the Internet and the increasing scale of digital images The bigger the case; the second is that different users have their own understanding of the content of the same image due to their respective worldviews and professional domain knowledge, so they make different semantic annotations for the image, which leads to the inconsistency of the semantic annotation of the image content

In order to extract the visual features of images and calculate the similarity of visual features between images, a large-scale calculation is required, and the visual features extracted by the image retrieval system based on image content are indistinguishable to humans, and do not have the visual and Semantically distinguishable, so it is difficult to describe image retrieval conditions

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