Image set unsupervised co-segmentation method based on deformable graph structure representation

A technology of structural representation and image set, applied in the field of image segmentation and computer vision, can solve problems such as difficulty in providing relevance

Inactive Publication Date: 2014-04-02
BEIHANG UNIV
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Problems solved by technology

Large-scale superpixels only provide fuzzy features, and

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  • Image set unsupervised co-segmentation method based on deformable graph structure representation
  • Image set unsupervised co-segmentation method based on deformable graph structure representation
  • Image set unsupervised co-segmentation method based on deformable graph structure representation

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Embodiment Construction

[0035] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0036] figure 1 The overall processing flow of the unsupervised co-segmentation method for image sets based on deformable graph structure representation is given.

[0037]This paper invents an unsupervised co-segmentation method for image sets based on deformable graph structure representation. The main steps are as follows:

[0038] 1. Create an inner image for each image in the atlas

[0039] (1) Input the original atlas with a unified size of 256*256 pixels. The original image is segmented using SLIC superpixels. Denote as P={p 1 ,p 2 ...p n}. The number of splits is 80 and the degree of compaction is 10. Such as figure 2 shown. Features are extracted using CIE Lab or FREAK descriptors for each superpixel.

[0040] (2) For each image, use the center of the superpixel as the vertex and use Delaunay triangulation to form a two-dime...

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Abstract

The invention discloses an image set unsupervised co-segmentation method based on deformable graph structure representation. The method is divided into the following four steps: firstly, superpixel segmentation is carried out on each graph in a graph set and a descriptor is extracted; secondly, an inner graph based on a Biharmonic distance is built for each picture, specifically, a two-dimensional image is stretched as a three-dimensional grid, and a Laplacian matrix is established on the three-dimensional grid according to a Riemann flow pattern space manner; thirdly, a deformable hypergraph which includes all superpixel in the graph set is established after the inner graph is established and the Biharmonic distance is calculated in a descriptor space(characteristic space); fourthly, clustering ideas considering connectivity are used, and an energy function which comprises the inner graph, the hypergraph and segmentation size constraints is established. Optimization is carried out by using the expectation maximization algorithm to obtain a final co-segmentation result. The algorithm partly uses CPU(Central Processor Unit) parallelism with CPU parallelism, the co-segmentation method has excellent segmentation accuracy and efficiency performance for the large-scale graph set.

Description

technical field [0001] The invention relates to the technical field of image segmentation in the field of computer vision, in particular to an image set unsupervised co-segmentation method based on a deformable graph structure representation. It co-segments the thermal diffusion information of image superpixels. Background technique [0002] Image segmentation technology (segmentation foreground) is a basic technology in the field of computer vision, and it has been a research hotspot because of its wide application. However, there has been no robust and mature method for non-learning and unsupervised automatic segmentation technology. The main reason is that it is difficult to automatically analyze and define appropriate constraints for various atlases with unpredictable characteristics. Since the concept of co-segmentation was first proposed in 2006, co-segmentation technology has attracted the attention of many scholars. The idea is that when an object appears at the sa...

Claims

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Application Information

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IPC IPC(8): G06T7/00
Inventor 赵沁平马际洲李帅郝爱民
Owner BEIHANG UNIV
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