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Image segmentation method and system

An image segmentation and image technology, applied in the field of image processing, can solve the problems of high dimensionality of similarity matrix, time-consuming calculation process, increased calculation time complexity, etc., and achieve the effect of high segmentation accuracy and short calculation time

Active Publication Date: 2016-12-07
SHAANXI NORMAL UNIV
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Problems solved by technology

[0003] However, graph-based semi-supervised classification methods still need to be further improved when applied to image classification
In this type of method, the dimension of the similarity matrix is ​​high, and the calculation process involves multiple matrix multiplication and inverse operations, and the calculation process is very time-consuming.
As the problem size increases, the computational time complexity also increases

Method used

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Embodiment Construction

[0045] In one embodiment, the image segmentation method used comprises the following steps:

[0046] S100. Extracting superpixels from the image to be segmented and obtaining the region center point of the superpixels;

[0047] S200. Use the marked sample points to mark the area center point of the superpixel;

[0048] S300. Mark all unmarked sample points by using the marking of the region center points of the superpixels, so as to realize image segmentation.

[0049] The core of this embodiment is to extract superpixels from the image to be marked, first mark the region center points of the superpixels, and then use the region center points of the superpixels to mark the remaining unmarked sample points. The division of superpixels is used to miniaturize the size of the image to be marked and reduce the computational complexity.

[0050] Preferably, the region center point of the superpixel is extracted by a simple linear iterative clustering (SLIC) algorithm. Using the S...

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Abstract

The invention relates to an image segmentation method and system. A super pixel is extracted from an image to be segmented, a regional center point of the super pixel is obtained and marked, and mark information of the regional center point of the super pixel is used to mark unmarked sample points. When the regional center point of the super pixel is marked, sparse simplification is carried out on an undirected weighted graph constructed by the sampling points, and a graph based semi-monitoring learning algorithm is used for marking. When the other unmarked sampling points are marked, the unmarked sampling points are marked in a classified manner by utilizing K-neighbor and taking the regional center of the super pixel as basis. The method and system are characterized by being high in speed, high in marking accuracy and suitable for large-scale image segmentation.

Description

technical field [0001] The present disclosure relates to image processing, in particular to an image segmentation method and system. Background technique [0002] Image segmentation is one of the fundamental problems in computer vision and has great application potential. Image segmentation is to extract the image target in the image, that is, a collection of pixels. The pixels in this collection satisfy certain similarities in terms of color, intensity, texture and other characteristics, so as to realize the positioning and recognition of the boundary in the image. In recent years, image segmentation has made some research progress. Among the proposed methods, graph-based semi-supervised methods have attracted extensive attention. [0003] However, graph-based semi-supervised classification methods still need to be further improved when applied to image classification. In this type of method, the dimension of the similarity matrix is ​​high, and the calculation process i...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T2207/20081G06T2207/30204
Inventor 马君亮汪西莉肖冰
Owner SHAANXI NORMAL UNIV
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