Labeling algorithm for biomedical image based on invisible dirichlet model

Abstract

The invention provides a labeling algorithm for a biomedical image based on LDA (Lejeune Dirichlet Allocation), mainly aiming at that the biomedical image is labeled, each image has a corresponding text file in a biomedical image language database and the particularity is combined. The LDA is used for extracting a subject term from captions of the image; then a context is extracted from the corresponding text file of the image according to the captions; and finally, the LDA is used for modeling the context; an obtained subject term is used as a final label of the biomedical image. The labeling algorithm has the beneficial effects that the biomedical image is labeled and the captions and the text file which are related to the image in a data set are sufficiently utilized to dig label words of the image; the accuracy is high and the plurality of label words can be generated at one time. After the biomedical image is accurately labeled, the related image is searched by using keyword index; the labeling algorithm is convenient and rapid and meets a text retrieval habit of people.

Description

technical field [0001] The invention relates to an image labeling algorithm, in particular to a biomedical image labeling algorithm based on an invisible Dirichlet model. Background technique [0002] With the development of digital imaging technology and the increasing popularity of photographic devices such as digital cameras, the number of various images is increasing exponentially. At the same time, the rapid development of the Internet has also made image dissemination and sharing faster. In order to effectively organize, query and browse such large-scale image resources, image retrieval technology has emerged as the times require, and has become a research focus in the field of computer vision. [0003] Existing image retrieval methods are mainly divided into two types: content-based image retrieval (Content-Based Image Retrieval) and text-based image retrieval (Text-Based Image Retrieval). Content-based image retrieval requires the user to provide an image...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the time-consuming and labor-intensive task of manual labeling, especially in the face of large-scale network images, it is already incapable, and the method of classification is used for image labeling, there are errors, and for images with multiple labels, it is necessary to Design multiple classifiers and classify images multiple times, and the efficiency is not high. The one provided is mainly for biomedical image annotation. In the biomedical image corpus, each image has a corresponding text file , combined with this particularity, a biomedical image annotation algorithm based on LDA (Stealth Dirichlet Allocation) is proposed, using LDA to extract the subject words from the caption of the image, and then according to these subject words from the image The context is extracted from the corresponding text file, and finally LDA is used to model the context, and the obtained keywords are used as the final annotation of the biomedical image

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