Image sequence category labeling method based on mixed graph model

A technology of image sequence and mixed graph, applied in the field of computer vision, can solve problems such as image sequence labeling that has not yet been applied

Inactive Publication Date: 2015-09-02
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In a sequence of images, multiple superpixels have a high-order, asymmetric relationship in the time domain, so they cannot be well described by simple edges
Hyperedges in hypergraphs can be used to describe high-order, asymmetric relationships between multiple variables, but traditional hypergraph models are solved based on spectral clustering methods, which are suitable for solving clustering problems and have not been applied to images. Class Labeling Problems for Sequences

Method used

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  • Image sequence category labeling method based on mixed graph model
  • Image sequence category labeling method based on mixed graph model
  • Image sequence category labeling method based on mixed graph model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0119] Treat labeled image sequences figure 1 (a) The simple graph model and the method of the present invention are respectively used for processing. It can be seen from the result comparison diagram that the method of the present invention effectively improves the accuracy and timing consistency of sequence image category labeling.

Embodiment 2

[0121] Image to be detected figure 2 (a) The simple graph model and the method of the present invention are respectively used for processing. It can be seen from the result comparison diagram that the method of the present invention effectively improves the accuracy and timing consistency of sequence image category labeling.

[0122] Table 1 is the accuracy rate (Prec.) on the KITTI data set by using the simple graph model and the method of the present invention, the recall rate (Rec.), the F standard (FM) evaluation numerical results, the higher the value, it shows that the method obtains The labeling results are better. It can be seen from Table 1 that the method of the present invention achieves better results in quantitative indicators than the simple graphical model method.

[0123] Table 1 Comparison data of image sequence experiment results

[0124]

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Abstract

The invention discloses an image sequence category labeling method based on a mixed graph model. The method comprises a step of performing superpixel segmentation of an image sequence and characteristic description of superpixels; a step of performing nearest neighbor matching of inter-frame superpixels of a two-continuous-frame image; a step of using the mixed graph model to carry out global optimization modeling of the image sequence category labeling based on the spatial domain adjacency relation among superpixels of a single frame image and the time domain matching relation among superpiexels of a multi-frame image; and a step of using a linear method to solve a global optimization problem to obtain category labels of superpixles of a continuous multi-frame image. Compared with previous graph models, the mixed graph model created by the invention can describe the first-order and symmetric relation among superpixels in a single frame image and also the high-order and non-symmetric relation among superpixels in a two-continuous-frame image; the linear method is used for solution; and a category label which is better in consistency of time domain and higher in accuracy is effectively provided for each superpixel of an image sequence.

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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06V10/751G06F18/2163
Inventor 黄文琦龚小谨刘济林
Owner ZHEJIANG UNIV
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