Multi-scale image segmenting method

Abstract

The invention provides a multi-scale image segmenting method. The multi-scale image segmenting method comprises the following specific steps: (1) inputting an original image, pre-segmenting the original image, and establishing a normalized mean offset histogram of each pre-segmented region by a kernel density estimation method; (2) calculating to obtain color similarity values of two adjacent regions; (3) combining the regions to generate a binary tree; and (4) selecting nodes in the binary tree for performing image segmentation. By the multi-scale image segmenting method, the problems of excessive segmentation in the image segmenting process, easy influence of noises in an image on the segmenting method, and high complexity of the segmenting method are solved; and multi-scale segmenting results are helpful to improving working efficiencies of subsequent image analysis, image recognition and other advanced processing stages.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to a multi-scale image segmentation method. Background technique [0002] Image segmentation is the process of dividing an image into several image regions with consistent features and non-overlapping features. Ideal image segmentation can extract "image objects" corresponding to the real world, thus making higher-level image understanding possible. Currently existing image segmentation methods include: segmentation methods based on Mean Shift (see literature: Comanicu D, Meer P. Mean shift: A robust approach toward feature space analysis. IEEE Trans on Patten Analysis and Machine Intelligence, 2002 , 24(5):603-619.), this method uses the gradient of the pattern space density function to realize the color clustering of the feature space, so as to achieve the purpose of image segmentation. The disadvantage of this method is that the phenomenon of over-segmentation ...

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Problems solved by technology

[0003] The present invention proposes a multi-scale image segmentation method, which solves the problems of serious over-segmentation in existing segmentation methods, the segmentation method is easily disturbed by noise in the image, and the complexity of the segmentation method is high; its multi-scale segmentation results contribute to Improve the work efficiency of subsequent image analysis, image recognition and other advanced processing stages

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