Image segmentation method based on rapid density clustering algorithm

Abstract

The invention discloses an image segmentation method based on a rapid density clustering algorithm. The image segmentation method comprises the following steps: 1) for a natural image to be processed, firstly carrying out preprocessing and initialization, comprising filtering noise reduction, gray-level registration, area dividing, scale zooming and the like; 2) then carrying out calculation of similarity distance between data points on sub-graphs completing scale variation, and obtaining correlation between pixel points; 3) then carrying out concurrent segmentation processing in each sub-graph, comprising drawing a decision graph based on the density clustering algorithm, carrying out residual analysis to determine a clustering center based on the decision graph and comparing based on the similarity distance to classify remaining points on an original scale sub-graph; and 4) then merging the sub-graphs after the segmentation is completed, and carrying out secondary re-clustering to obtain a segmentation result graph with original size dimensions. The image segmentation method based on the rapid density clustering algorithm for parameter robust provided by the invention can automatically determine the number of segmented classes to realize relatively high segmentation accuracy rate.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image segmentation method. Background technique [0002] Image segmentation is one of the key technologies of image processing, which is widely used. The main task is to divide the image into several specific and unique regions, which have strong similarities within the regions and strong differences between the regions. At the same time, image segmentation is also an important step in data analysis and understanding of images, and is the key from image processing to image analysis. The existing image segmentation methods are mainly divided into the following categories: boundary-based segmentation methods, region-based segmentation methods, and specific theory-based segmentation methods. After the image segmentation is completed, the extracted objects can be used in image semantic recognition, image search and other fields, so the quality of image segmen...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of the existing image segmentation methods based on clustering algorithms, which are sensitive to cluster centers, large parameter dependence, poor adaptability, and the number of segmented clusters cannot be accurately and automatically determined, the present invention provides a method that can automatically determine the segmentation category. Image segmentation method based on fast density clustering algorithm with high segmentation accuracy and robustness to parameters

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