Scene image classification method based on interval Type-2 fuzzy support vector machine

Abstract

The invention discloses a scene image classification method based on an interval Type-2 fuzzy support vector machine, and relates to the technical field of general image data processing or producing methods. According to the method, a novel fuzzy support vector machine design method based on an interval Type-2 fuzzy set is put forward, and a scene image is effectively classified based on three steps, namely, image classification feature extraction, feature dimension reduction and feature classification. According to the invention, uncertain information is described and processed based on an interval Type-2 fuzzy set, an interval Type-2 fuzzy support vector machine classification model is built, and the accuracy and robustness of scene image classification are improved.

Description

technical field [0001] The present invention relates to the technical field of general image data processing or generation methods, in particular to a scene image classification method based on an interval Type-2 fuzzy support vector machine. Background technique [0002] With the development of digital technology and the application of digital equipment, the number of images in various image databases has shown explosive growth. It is urgent to extract image semantic information that people can understand through computers, so as to facilitate users to automatically manage various digital images. , organization and analysis. How to effectively manage and quickly retrieve these images is a huge challenge. The classification of scene images is the key technology to solve this problem. Scene image classification, that is, to complete the automatic recognition of image scene categories (such as offices, corridors, streets, cities, high-rise buildings, etc.) based on the featu...

AI Technical Summary

Problems solved by technology

[0005] However, the standard support vector machine model cannot effectively represent and process the uncertain information (such as noise changes, shooting angle changes, lighting conditions, etc.) that are ubiquitous in scene images, so it is difficult to obtain accurate classification results

Considering system uncertainties, how to introduce expert experience knowledge according to fuzzy theory, how to construct flexible fuzzy kernel function, how to design systematic fuzzy support vector machine (FuzzySupportVectorMachine, FSVM) model and other issues, the existing fuzzy support vector machine method is still less discussed in this area

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