Minimum Spanning Tree Image Segmentation Model Based on New Weights

Abstract

The quality of image segmentation results directly affects subsequent image processing and its application, therefore image segmentation is the most crucial step in image processing and recognition. A minimum spanning tree image segmentation method using new weights is proposed to address the issues of low efficiency and low segmentation accuracy in traditional image segmentation methods. The study first proposed a minimum spanning tree construction method based on color vector angle distance color difference measurement, which compensates for the non-uniformity of color space through dynamic weight adjustment, and improves on the fast multi spanning tree segmentation algorithm with adaptive threshold. The proposed decomposition method preprocesses the image to reduce the image nodes. The experiment showed that the research method effectively improved color difference measurement accuracy. The segmentation accuracy, over segmentation rate, and under segmentation rate obtained by the research method outperformed other segmentation methods in terms of average values, with an average of 0.984, 0.059, and 0.023, respectively. Compared to other minimum spanning tree segmentation algorithms, the research method improved the average segmentation time by 0.46 seconds, 0.49 seconds, 2.04 seconds, and 3.79 seconds, respectively. The segmentation algorithm studied in this study has good segmentation performance and improves the efficiency of image segmentation, which has certain practical application value in various image segmentation fields.