A self-adaptive image target region segmentation method based on SLIC

Abstract

The invention discloses a self-adaptive image target area segmentation method based on SLIC, and relates to a superpixel segmentation technology. The objective of the invention is to solve the problemof low segmentation efficiency caused by the fact that parameters need to be manually input when super-pixel segmentation is carried out on a main body image containing a plurality of targets by a traditional super-pixel segmentation method. The method comprises the steps that firstly, super-pixel pre-segmentation processing is conducted on an image through SLIC, then super-pixel units are established with pre-segmented super-pixel points as center points, and the super-pixel measurement units comprise the gray level, the position and the Hash value; The measurement unit is used as a super-pixel parameter, and finally, through a distance-based clustering algorithm of adaptive parameters, the segmented too small regions are combined, so that the super-pixels are clustered into a determinedmain body and an obvious main body segmentation boundary. According to the method, a user does not need to carry out setting input, and the number of types of superpixels needing to be segmented is determined through a method of calculating the image complexity. The method is suitable for the fields of target recognition, mode recognition and artificial intelligence.

Description

technical field [0001] The invention relates to image processing technology, in particular to superpixel segmentation technology. Background technique [0002] In recent years, people's research on superpixels has become increasingly popular, especially in many aspects of machine vision, which has set off a wave of research on superpixels. Image segmentation has a very wide range of applications in actual processing. Recognition, pattern recognition, artificial intelligence and many other fields have been widely used. However, as the size of the image becomes larger and larger, the calculation efficiency of directly processing the image at the pixel granularity level is low, which requires reducing the number of pixels and expanding the meaning represented by the pixels, so it is necessary to use super-pixels based on the original pixels. Instead of pixels, superpixel segmentation is the process of clustering pixels into superpixels. It saves the boundary information of th...

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Problems solved by technology

[0003] The purpose of the present invention is to solve the problem of low segmentation efficiency due to the need to manually input parameters and set the number of segmentations when the traditional superpixel segmentation method performs superpixel segmentation on a subject image containing multiple targets. SLIC (Simple Linear Iterative Clustering) Adaptive Image Object Region Segmentation

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