Semantic marking method for image scene based on geodesic transmission

Abstract

The invention provides a semantic marking method for an image scene based on geodesic transmission, which comprises the steps of: obtaining a rough semantic probability graph corresponding to an image scene by an object judging method; estimating the color characteristic distribution and the boundary characteristic distribution of the image scene; defining a geodesic distance on a mixed flow pattern by combining the color characteristic distribution with the boundary characteristic distribution; determining a point set with maximal local probability by a mean-shift algorithm as an initial seed point of geodesic transmission for various semantic categories; and determining the shortest geodesic distance of each point in the scene by a quick transmission algorithm based on a priority sequence for the geodesic distances of the defined various semantic categories so as to obtain the accurate semantic mark of the image scene. The invention can be extensively applied in semantic informationmarking of computer vision systems in fields of military, aviation, aerospace, monitor and manufacture, and the like.

Description

technical field [0001] The invention relates to the fields of computer graphics and computer vision, in particular to a method for semantically marking image scenes based on geodesic propagation. Background technique [0002] Image scene semantic labeling is an important part of image understanding and image search, so it has become one of the hotspots of researchers in recent years. However, due to the complexity and variety of image scenes, image scene understanding is an important problem that is very difficult to solve. The understanding of image scenes often requires not only the correct interpretation of individual objects and the relationship between objects, but also the ability to effectively solve the problem of image diversity. [0003] The existing image scene semantic labeling methods generally define the energy function of the scene labeling problem on the neighbor system similar to Markov random field and conditional random field, and use Swendsen Wang Cuts (...

AI Technical Summary

Problems solved by technology

This method proposes a dynamic relationship transfer algorithm on the hierarchical structure to solve the problem of rough semantic probability optimization. However, this method does not consider the use of image local features to improve the accuracy of image labeling.

[0007] These existing mathematical models and algorithms have similar graph structure representation and energy function composition, and the speed of these methods cannot meet the requirements of real-time image labeling

These methods can achieve a certain object recognition rate on public data sets, but these methods only roughly give the semantic probability of each pixel of the image, and have not completed accurate semantic labeling

Due to the slow processing speed and rough image labeling severely restrict the popularization and application of the algorithm, it is particularly important to study accurate and fast semantic labeling methods

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