Image segmentation method and system based on dynamic multi-objective optimization

Abstract

The invention discloses an image segmentation method and system based on dynamic multi-objective optimization. The method comprises the steps of: constructing a multi-objective function by using a K-Means algorithm and a Fuzzy C-means (FCM) algorithm; defining an environmental change rule by using a background difference method; constructing a self-adaptive inertial dynamic factor; optimizing a timely mutation factor; based on the self-adaptive inertial dynamic factor and the timely mutation factor, dynamically optimizing the multi-objective function by using a multi-objective optimization particle swarm method. The K-Means and the FCM are optimized by dynamically optimizing a particle swarm algorithm, thus a good aggregation result can be acquired; the problem of error segmentation of pixels or edge blur is avoided, thus a good image segmentation effect can be achieved; the image segmentation is high in accuracy rate; the image segmentation method and system based on dynamic multi-objective optimization can provide high-quality result data and technical reference for image recognition.

Description

technical field [0001] The invention relates to image processing technology, in particular to an image segmentation method and system based on dynamic multi-objective optimization. Background technique [0002] The application of the Internet of Things in the field of security has attracted attention, and has achieved phased results in related technologies, among which the identification of moving objects in video images in the community has become one of its research hotspots. For the recognition of moving objects, digital image processing technology can be used to realize image recognition, or multi-objective optimization combined with image processing methods can be optimized to obtain the best results. [0003] At present, the algorithm strategy achieves the purpose of image segmentation through clustering technology from different angles. Although the use of multi-objective technology is mentioned, there is no use of dynamic factors in multi-objective optimization to re...

AI Technical Summary

Problems solved by technology

Generally, single-objective or multi-objective optimization methods are used to segment images, which can achieve a certain accuracy in a static environment, but when the environment changes or for moving objects, it cannot effectively adapt to dynamic changes, resulting in the segmentation of dynamic images. and identified errors or errors

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