An image adaptive clustering method based on visual bionics and force field

Abstract

A kind of image self-adaptive clustering method based on visual bionics and force field effect of the present invention, comprises the following steps: Step 1, select an image as the image Img to be analyzed, and generate the image Img to be analyzed under the CIE-Laba color space Color map Img2; step 2, divide each band of the color map Img2 under the CIE‑Laba color space into d subspaces, and obtain d n color subregion space, where n is the band number of the color map Img2; step 3, calculate d n The stable center of gravity of each subregion in the color subregion space, and the stable center of gravity of each subregion is used as the clustering center; the present invention proposes and realizes an adaptive clustering method based on visual bionic technology and force field effect, This method is efficient, concise, and easy to implement, and can be used for preliminary cognition of various data analysis.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an adaptive clustering method based on visual bionic technology and force field effects. Background technique [0002] Image analysis is the most basic technical means in the hot machine vision and artificial intelligence. As the most basic image analysis method, image clustering is widely used in various image segmentation, classification, and cognition as a preliminary analysis. Among them, cluster analysis refers to the analysis process of grouping a collection of physical or abstract objects into multiple classes composed of similar objects. The goal of cluster analysis is to collect data to classify on the basis of similarity. In different application fields, many clustering techniques have been developed. These technical methods are used to describe data, measure the similarity between different data sources, and classify data sources into different c...

AI Technical Summary

Problems solved by technology

However, in many practical data analysis applications, the acquisition of initial prior information itself is a paradoxical problem: that is, the purpose of analyzing data is to obtain information, and it is difficult to analyze data without information.

However, in order to estimate the prior information, the improved method pays a lot of complicated calculations, and abandons the advantages of high efficiency of the clustering algorithm itself, making it difficult to apply to a wide range of actual data analysis work.

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