Subtractive clustering based rapid image segmentation method

Abstract

The invention discloses a subtractive clustering based rapid image segmentation method. The method comprises the following steps: firstly, normalizing all pixel points to a hypercube, and carrying out equal-interval uniform sampling and then carrying out restructuring on all the pixels to be clustered; and in the restructured pixels, calculating a density weight matrix and an inverse matrix thereof between every two sampling pixel points and density weight matrixes among sampling pixels and the rest of non-sampling pixels; then, calculating a density weight matrix between every two approximate non-sampling pixel points, and calculating the density values of all the pixels; and finally, calculating the maximum density value of all the pixels and obtaining a clustering center, and for finding out a new clustering center, necessarily carrying out attenuation on the density value of each pixel point, carrying out incremental iteration on the process, and stopping iterating according to termination conditions. Compared with the classical subtractive clustering method, the method disclosed by the invention greatly improves the real-time property of a subtractive clustering method for large-scale data sets under the condition of not affecting clustering results.

Description

technical field

[0001] The invention relates to the technical field of digital image processing, in particular to a fast image segmentation method based on subtractive clustering. Background technique

[0002] Digital image segmentation is an important technology in the field of digital image processing. Image segmentation is the subdivision of an image into its constituent sub-regions or objects. It is often regarded as an important link embedded in the processing of the machine vision system. k Mean Clustering, Fuzzy c Mean clustering is often used for digital image segmentation and is a commonly used method. However due to k Means clustering and fuzzing c The two image segmentation methods of mean clustering are very sensitive to the initial cluster center, and different initial values ​​can easily get different results. Since subtractive clustering has the characteristic of obtaining the initial cluster center, it can be compared with k mean clustering or fuzzing ...

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