Unsupervised video segmentation method based on non-local space-time characteristic learning

Abstract

The invention discloses an unsupervised video segmentation method based on non-local space-time characteristic learning. The method comprises the following steps of acquiring a video sequence which needs to be segmented; using a superpixel to segment and process the video sequence; using a light stream to carry out previous and next frame information matching; according to adjacent frame information of the video sequence, acquiring a range of a motion target and taking as model initialization input; using global information to optimize a matching result; and establishing a graph model and using a graph segmentation algorithm to solve a segmentation result, and through video segmentation, acquiring output of the motion target. Through carrying out superpixel segmentation on each frame of image in an input video, an operation complexity can be greatly reduced; and non-local space-time information is used to optimize matching information acquired through the light stream so that segmentation robustness can be increased and a noise influence is reduced. Any manual intervention is not needed, and an accurate segmentation result can be acquired completely based on video image information.

Description

technical field [0001] The invention relates to an unsupervised video segmentation method based on non-local spatio-temporal feature learning, belongs to the field of computer vision, and specifically relates to the field of video segmentation in image processing. Background technique [0002] Video refers to an image sequence composed of a series of continuous single images, and usually includes text, voice and other information. In order to facilitate transmission and use, it is usually necessary to segment the video, remove some areas in the video that are not of interest to the user, and obtain the data characteristics of the target content for subsequent feature extraction and analysis. [0003] Video segmentation, also known as motion segmentation, refers to dividing an image sequence into multiple regions according to a certain standard, and its purpose is to separate meaningful entities from the video sequence. In image processing technology, image and video segment...

AI Technical Summary

Problems solved by technology

In general, the difficulty of segmentation lies in the irregular movement and deformation of the segmented target, the rapidly changing complex background, the inaccurate motion information and the blurring of the target, etc., but to obtain accurate information, it is necessary to use accurate segmentation results. caught in a loop

So far, there is no general and reliable unsupervised segmentation algorithm that can be applied to all complex transformation scenes. At present, most of the video segmentation algorithms proposed by many scholars at home and abroad are aimed at a specific application or a specific type of image. video

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