Multi-threshold image segmentation method based on cooperative quantum particle swarm algorithm

Abstract

The invention discloses a multi-threshold image segmentation method based on a cooperative quantum particle swarm algorithm. The multi-threshold image segmentation method provided by the invention comprises the following steps of: (1) depending on an optimal segmentation threshold, establishing and initializing a first generation of partial swarm; (2) depending on an adaptability function of the multi-threshold segmentation, calculating an adaptability value of each particle, and calculating an individual optimal position of each particle as well as an overall optimal position of all the particles; (3) updating a position vector of each of the particles by a cooperative quantum-behaved particle swarm iteration formula, as well as the individual optimal position of each particle and the overall optimal position of all the particles; and (4) repeating the steps (2) and (3) until satisfying iteration times of the particle swarm iteration formula. According to the image segmentation method, the multi-threshold resolving speed of a target function based on the maximum between-class variance, and the segmentation efficiency are improved.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a multi-threshold image segmentation method based on a cooperative quantum particle swarm algorithm. Background technique [0002] Threshold segmentation method is a region-based image segmentation technology, its basic principle is: by setting different feature thresholds, the image pixels are divided into several categories. Commonly used features include: grayscale or color features directly from the original image; features transformed from original grayscale or color values. [0003] The maximum inter-class variance threshold segmentation method is one of the threshold segmentation algorithms. Its basic idea is to divide the histogram into two groups at a certain closed value, and calculate the variance information of the two groups, because the variance is the uniformity of the gray distribution A measure of , the larger the variance value, the greater the difference betwee...

AI Technical Summary

Problems solved by technology

Specifically, none of the above algorithms have considered overcoming the limitations of the genetic algorithm, ant colony algorithm, and particle swarm optimization algorithm itself, such as the low convergence speed of the genetic algorithm and ant colony algorithm; although the particle swarm optimization algorithm (PSO) has a fast convergence speed, it is easy to The disadvantage of falling into a local minimum; based on the quantum particle swarm optimization algorithm (QPSO), although the global search ability is strong, it still has the problem of dimensionality constraints

Considering the running process of PSO and QPSO, it can be seen that when the algorithm updates the solution vector at each step, all dimension vectors are updated at the same time, which may cause some parts of the vector to be closer to the real solution, but it is also possible that the rest The solution may be far from the true solution

The QPSO and PSO algorithms only consider a global change, ignoring the regression of the local dimensional solution, so they have the defect of being bound by the dimension.

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