Image segmentation method based on hierarchical higher order conditional random field

A conditional random field and image segmentation technology, applied in image analysis, image enhancement, image data processing, etc., can solve the problems of segmentation result dependence, inappropriate segmentation granularity, random field model of segmentation accuracy, etc.

Inactive Publication Date: 2016-02-10
XI AN JIAOTONG UNIV
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Problems solved by technology

Although this method has the characteristics of fast operation, the conditional random field model based on superpixels [4] usually enforces the consistency of the classification labels of all pixels in a superpixel, resulting in the segmentation results relying heavily on unsupervised

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  • Image segmentation method based on hierarchical higher order conditional random field
  • Image segmentation method based on hierarchical higher order conditional random field
  • Image segmentation method based on hierarchical higher order conditional random field

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Embodiment Construction

[0028] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0029] 1. Extraction of image pixel-level features

[0030] (1) Texture features

[0031] The present invention adopts the method based on the filter group proposed by Malik et al. First, the image is converted from the RGB color space to the CIE-Lab color space, and then a 17-dimensional multi-channel multi-scale Gaussian filter group is used to extract each pixel point The texture information, the filter set includes the basic Gaussian model filter under different scales and channels, the first-order partial derivative filter in the X and Y directions, and the Laplacian filter, then each pixel is associated with a 17 dimensional feature vector, each vector contains the area texture information of the corresponding pixel. Finally, a pixel is associated with a 17-dimensional vector, which is used as the texture feature of the image....

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Abstract

An image segmentation method based on a hierarchical higher order conditional random field model is provided, comprising: firstly, extracting a multi-class texture feature for a target image, and constructing a one-variable potential function and a pairing potential function of a pixel level; then, acquiring super pixel fragments of different granularities by using a unsupervised segmentation algorithm; designing a one-variable potential function and a pairing potential function of a super pixel level corresponding to each granularity layer; constructing a hierarchical higher order conditional random field model; learning a parameter of the hierarchical higher order conditional random field model in a supervision manner by using a manual marked sample; and finally, acquiring a final segmentation marking result for a to-be-tested image by means of model reasoning. The hierarchical higher order conditional random field model used in the present invention fuses multi-feature texture information and multi-layer super pixel segmentation information of an image, and can effectively improve boundary segmentation accuracy of a multi-target object in the image.

Description

technical field [0001] The invention relates to the technical field of image segmentation, in particular to an image multi-object segmentation method based on hierarchical high-order conditional random fields. Background technique [0002] Image segmentation is a key problem in the field of computer vision. The quality of image segmentation has an important impact on subsequent applications such as image content analysis and pattern recognition. The current image segmentation algorithms mainly include the following categories: 1) Image segmentation based on threshold. This type of method is suitable for target images where the target and background have different gray scale ranges. 2) Region-based image segmentation. Its idea is based on the pixels with similar characteristics, through the image segmentation technology of region growing and region merging. 3) Segmentation based on deformation model. Such methods need to give the initial closed segmentation curve of the ...

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Application Information

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IPC IPC(8): G06T7/00G06T7/10
CPCG06T2207/10004G06T2207/20081
Inventor 杨旸谢明远刘跃虎
Owner XI AN JIAOTONG UNIV
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