Video significance detecting method based on area segmentation

A region segmentation and detection method technology, applied in the field of image processing, can solve the problems of low accuracy and efficiency, simple time domain structure, weak sequence relationship, etc., to achieve the effect of enhancing the representation ability and good experimental effect

Active Publication Date: 2016-08-31
XIDIAN UNIV
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Problems solved by technology

[0009] The above video salient region extraction methods all add motion features to the saliency model in the image domain. The time domain structure is too...

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  • Video significance detecting method based on area segmentation
  • Video significance detecting method based on area segmentation
  • Video significance detecting method based on area segmentation

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Embodiment Construction

[0031] refer to figure 1 , the video saliency detection method based on region segmentation of the present invention, comprises the following steps:

[0032] Step 1, obtain the superpixel block of the video sequence.

[0033] 1.1) Framing the video in the video segmentation database to obtain a video sequence;

[0034] 1.2) Carry out simple linear iterative clustering SLIC on the video sequence to obtain the superpixel blocks of the video sequence. This simple linear iterative clustering algorithm performs clustering according to the color similarity and proximity between pixels, not only considering the pixel The spatial distance between points also takes into account the difference of their color information.

[0035] Step 2, extract the static features of the superpixel block.

[0036] The static features of each superpixel block include color feature values, histogram feature values ​​and texture feature values.

[0037] refer to figure 2 , the specific implementatio...

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Abstract

The invention discloses a video significance detecting method based on area segmentation, wherein the method mainly settles a problem of low detecting accuracy by an existing video significance detecting method. The video saliency detecting method comprises the steps of 1, performing linear iteration clustering on video frames, thereby obtaining a super-pixel block, and extracting the static characteristic of the super-pixel block; 2, by means of a variational optical flow method, obtaining the dynamic characteristic of the super-pixel block; 3, fusing the static characteristic and the dynamic characteristic for obtaining a characteristic matrix, and performing K-means clustering on the characteristic matrix; 4, performing linear regression model training on each cluster, thereby obtaining a regression model; and 5, reconstructing a mapping relation between a test set sample and a obtaining the significance value of a test set super-pixel block, and furthermore obtaining the significance graph of a testing sequence. Compared with a traditional video significance algorithm, the video significance detecting method has advantages of improving characteristic space and time representation capability, and reducing effect of illumination to detecting effect. The video significance detecting method can be used for early-period preprocessing of video target tracking and video segmenting.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a video saliency detection method, which can be used for target tracking, object recognition and video segmentation. Background technique [0002] When computers deal with complex scene problems, the complexity of the background makes some existing methods unable to handle the scene better. Studies have found that the human visual system can easily understand various complex scenes, so the working principle of the human system can be used for reference when dealing with problems related to complex scenes. Scholars have conducted in-depth research and reasoning on the selection mechanism of human visual attention and obtained the theory of visual saliency. This theory believes that the human visual system only processes some parts of the image in detail, while almost turning a blind eye to the rest of the image. Based on this theory, relevant scholars in the field ...

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Application Information

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IPC IPC(8): G06T7/20
CPCG06T2207/10016
Inventor 韩冰魏国威仇文亮高新波张景滔王平
Owner XIDIAN UNIV
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