Adaptive clustering method of large number of high-dimensionality image local feature points

Abstract

The invention relates to an adaptive clustering method of a large number of high-dimensionality image local feature points. The method includes the following steps that: a scale space is constructed for an input image; a Gaussian difference scale space is constructed through using Gaussian difference kernels of different scales and image convolution; each sampling point is compared with all points adjacent to the sampling point; the positions and scales of feature points are determined accurately through fitting a three-dimensional quadratic function; Gaussian filtering is performed on the input image; the variance and Gaussian convolution kernel of a Gaussian filtering function are changed; the feature points are clustered; the value of a bias parameter is selected; the values of the influence degrees and membership degrees of sampling points are calculated; the values of the influence degrees and membership degrees are calculated constantly until an appropriate clustering center is found out; when the number of the times of calculation exceeds a set maximum value, or the clustering center does not change after a plurality of times of calculation, calculation is terminated; and curve fitting is performed on 10 groups of clustering results, the clustering classes of the feature points corresponding to the input image are found out, the feature points of the input image are clustered. With the method of the present invention adopted, the efficiency of image analysis is significantly improved.

Description

technical field [0001] The invention relates to an adaptive clustering method. In particular, it involves a large number and high dimensionality adaptive clustering method of image local feature points. Background technique [0002] With the development of high-speed computers and large-scale integrated circuits, digital image processing technology has made a series of gratifying breakthroughs and progress, and its results are widely used in many fields such as biomedical engineering, industrial manufacturing, space exploration, public security, culture and art, etc. . However, there are still many problems in digital image processing technology itself, which hinder its further development and promotion. Among them, the problem of large amount of data is a major problem faced by image processing technology at present. Images record scene information in the form of pixel arrays. An ordinary 1024×1024 uncompressed true-color image can record data up to 3MB. The huge amount ...

AI Technical Summary

Problems solved by technology

The clustering algorithm that adaptively determines the number of categories does not require user participation, and is very suitable for batch processing of data, but the algorithm has a large computational complexity, and it takes too long or even overflows when processing large amounts of data

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