Image scene classification method and system combined with semi-supervised clustering

Abstract

The invention discloses an image scene classification method and system combined with semi-supervised clustering, and the method comprises the steps of redefining an objective function of semi-supervised Kmeans through employing a labeled sample, and supplementing and defining an objective function of SVM, and obtaining semi-supervised Kmeans clustering and a base learning device based on SVM classification; enabling the two base learners to carry out cooperative training, and forming a selection and iterative training scheme of a pseudo label sample; and finally, according to the confidence coefficient, fusing results of the two learners to obtain a scene image category to which the sample belongs. According to the invention, different types of methods in the image scene classification field are used to construct a base classifier and carry out cooperative training. Meanwhile, a pseudo label sample is introduced to expand a training set, so that the problem of insufficient label samples is effectively solved. Furthermore, clustering is carried out on the label-free samples to obtain the distribution characteristics of the label-free samples, and the concept drift problem is solved. Finally, the labeling cost of the scene image is reduced, concept drift is solved, and the image scene classification accuracy is improved.

Description

technical field [0001] The invention relates to the field of image scene classification, in particular to an image scene classification method and system combined with semi-supervised clustering. Background technique [0002] Image scene classification means that the system classifies the provided image information to obtain the scene to which the image belongs, so as to achieve the purpose of image scene classification. At present, the research on image scene classification has made great progress. However, due to the large differences in the scene composition of different images, there are not only problems related to intra-class differences and inter-class similarities, but also complex and changeable target content often exists in scene images. This leads to a large difference in the classification effect of the same classification method on different scene datasets, that is, there is no classification method that can perform well in multi-class image scene databases. ...

AI Technical Summary

Problems solved by technology

[0008] The above-mentioned algorithms all have certain deficiencies. In order to obtain good classification results, a large number of expensive manually labeled training samples are often required, which leads to the problem of insufficient labeled samples.

At the same time, because there are a large number of unlabeled samples in the scene image, and as time goes by, the number of these unlabeled samples is increasing day by day, and the distribution of these samples also changes unpredictablely over time. At this time, the original classifier will not be able to adapt The changed scene image, which leads to the performance degradation of the classifier

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