Unsupervised video segmentation method integrated with temporal-spatial multi-feature representation

Abstract

The invention discloses an unsupervised video segmentation method integrated with temporal-spatial multi-feature representation. The features of a target are extracted and identified according to themotion information and saliency and color features of the target, and the target is segmented stably and accurately through a Gaussian mixture model. The method includes the following steps: super pixel segmentation; optical flow matching; optimizing the matching result; building a graph model, and calculating the super pixel-level segmentation result; using the segmentation result to train the parameters of a Gaussian mixture model; calculating the pixel-level segmentation result; and getting a final segmentation result according to the super pixel-level segmentation result and the pixel-level segmentation result. Through super pixel segmentation on each frame of image, the computational complexity is greatly reduced. The robustness of segmentation is improved by optimizing the optical flow matching information according to non-local temporal-spatial information. The introduction of the Gaussian mixture model makes up for the large edge matching error in the process of super pixel segmentation. The saliency feature further improves the accuracy and credibility of the segmentation result.

Description

technical field [0001] The invention relates to an unsupervised video segmentation method that combines temporal and spatial multi-feature representations, 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 ...

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

Images