Image Sequence Classification Method Based on Mixed Graph Model

Abstract

The invention discloses an image sequence category labeling method based on a mixed graph model, which includes: performing superpixel segmentation on the image sequence, and performing feature description on the superpixels; performing nearest neighbor matching on superpixels between two consecutive frames of images; based on The adjacency relationship between the superpixels of a single frame image in the spatial domain and the matching relationship in the time domain between the superpixels of multi-frame images are used to model the global optimization model of the image sequence category labeling problem using the mixed graph model; the global optimization problem is solved using a linear method, and we get Class labels of superpixels in continuous multi-frame images; compared with previous graph models, the hybrid graph model constructed by the present invention can describe the first-order and symmetric relationship between superpixels in a single frame image, and can also describe two consecutive frames of images The high-order, asymmetric relationship between superpixels is solved by a linear method, effectively endowing each superpixel of the image sequence with a class label with better temporal consistency and higher accuracy.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to an image sequence category labeling method based on a mixed graph model. Background technique [0002] The image sequence category labeling based on the probabilistic graph model is an important research problem in the field of computer vision. It is a key supporting technology for applications such as autonomous vehicle environment perception and smart city traffic system construction. Its purpose is to assign a category to each pixel in the image sequence Label. Image sequence category labeling is a labeling problem, and the probabilistic graphical model is an effective tool for global optimization of this type of labeling problem. The key idea of ​​the labeling problem based on the probabilistic graphical model is to regard the label to be solved as a random variable, and obtain the global optimal solution of the random variable by minimizing the energy of...

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