Video significance detecting method based on area segmentation

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 ...

AI Technical Summary

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 simple, and the relationship between sequences is weak. Although the salient region of the video can be extracted, the accuracy and efficiency are relatively low.

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