Semi-supervision image classification method based on weighted graph

Abstract

The invention relates to a semi-supervised image classification technology, which pertains to the field of computer multimedia technology, the method comprises the steps that: regional characteristics are extracted based on the segmentation of a digital image, the proportions of area of each region to the area of the entire image are calculated; the linear programming is used for constructing a weighted graph; the tag spread method is used for transferring class information of the tagged image on the weighted graph; finally, the non-tagged image is classified according to the final result of the transfer of the class information. The method uses the linear programming for constructing the weighted graph, wherein, a parameter needing to be set is the number of the neighborhood images, when the parameter changes in a larger range, the image classification result obtained by using the method is more stable, thus effectively overcoming the problem that the parameter in the method for constructing the weighted graph based on a Gaussian function has greater impact on the classification result.

Description

A Semi-supervised Image Classification Method Based on Weighted Graph technical field The invention belongs to the technical field of computer multimedia, in particular to semi-supervised image classification technology. Background technique Digital images refer to image information recorded in digital form. With the development of computer science and network technology, the number of digital images is increasing rapidly at an alarming rate, and they are playing an increasingly important role in people's daily life. In order to better process and utilize the information contained in massive digital images, it is necessary to classify digital images reasonably. The method of completely relying on manual classification of images is time-consuming and laborious, and the classification results will be affected by the subjectivity of the classifiers. In order to improve the speed and accuracy of image classification, content-based image classification technology came into be...

AI Technical Summary

Problems solved by technology

However, this method is only applicable to the case where the image feature lengths are equal

For region-based image classification, the number of regions obtained after different images are often divided is different, so the feature lengths of different images are also different. Therefore, it is not feasible to directly use the nearest neighbor linear reconstruction method.

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