Video image splitting method based on restrain spectral clustering and markov random field

A video image and spectral clustering technology, applied in image analysis, image data processing, instruments, etc., can solve the problem of not getting dense segmentation results

Active Publication Date: 2013-02-20
TSINGHUA UNIV
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Problems solved by technology

[0006] Traditional video segmentation methods based on spectral clustering often only use motion information or simply weight motion information and static information for segmentation, and cannot analyze

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  • Video image splitting method based on restrain spectral clustering and markov random field
  • Video image splitting method based on restrain spectral clustering and markov random field
  • Video image splitting method based on restrain spectral clustering and markov random field

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Embodiment Construction

[0058] The specific implementation manner of the invention will be further described below in conjunction with the accompanying drawings and embodiments. The following examples are only used to illustrate the present invention, but not to limit the scope of the present invention.

[0059] The purpose of the present invention is to solve the problem of accurately segmenting video images. Its core idea is to add motion information as a constraint to the spectral clustering segmentation framework, and combine the spatial smoothness constraints to construct a Markov random field model to accurately segment the sense of motion in the video image. target of interest. A video image segmentation method based on constrained spectral clustering and Markov random field in this embodiment, its flow chart is as follows figure 1 As shown in , it mainly includes the following steps:

[0060] S1. Use the optical flow method to extract the long-term motion trajectory of some pixels from the ...

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Abstract

The invention belongs to the technical field of computer vision, image treatment and pattern recognition, and particularly relates to a video image splitting method based on restrain spectral clustering and a markov random field. According to the method, a similarity matrix between two pixels is calculated based on the static characteristics of images, and the motion characteristic is added in a spectral clustering framework to be used as a restrain. In comparison with the conventional simple weighting method, two pieces of information with different reliability degrees are utilized well in the treatment method. Besides, as motion information coding is used as a restrain, only sparse point motion trains are required, so that reliable long-time motion information can be utilized to obtain an accurate and dense splitting result. Further, space smooth information of each pixel is coded into corresponding restrains by constructing a markov random field model, so that the video image splitting effect is relatively precise.

Description

technical field [0001] The invention relates to the technical fields of computer vision, image processing, pattern recognition and the like, in particular to a video image segmentation method based on constrained spectral clustering and Markov random field. Background technique [0002] Video segmentation refers to a technique that divides a video sequence into some non-overlapping spatio-temporal regions according to its characteristics. Video segmentation is a key technology of computer vision and the basis of many applications such as video surveillance, human-computer interaction and video editing. [0003] The basis of video segmentation is still image segmentation. Most still image segmentation techniques are performed in a bottom-up manner. They achieve the purpose of segmentation by detecting boundaries or clustering pixels based on features such as color and texture. However, due to the huge semantic gap between the underlying features of the image and the object ...

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Application Information

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IPC IPC(8): G06T7/20G06T7/215G06T7/277
Inventor 周杰胡瀚冯建江喻川张昊飏
Owner TSINGHUA UNIV
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