Viewpoint tracking method based on geometrical reconstruction and semantic integration

A geometric structure and geometric technology, applied in the field of viewpoint tracking based on geometric reconstruction and semantic fusion, can solve the problems of lack of semantic correlation description, lack of geometric structure of salient areas of images, and low practicability.

Active Publication Date: 2015-03-11
HEFEI UNIV OF TECH
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Problems solved by technology

[0005] For example, in 2010, Jia Li and other authors published a visual saliency prediction method in the article "Probabilistic Multi-Task Learning for Visual Saliency Estimation in Video" published in the top international journal International Journal of Computer Vision. This method uses multi-task framework to estimate visual saliency, and the multi-task is realized based on low-dimensional visual features and task-related factors. The semantic correlation description between them makes the saliency model unable to restore the global and local information of the image, resulting in the loss of the extracted local or global information of the image;
[0006] Two: Many viewpoint tracking technologies lack the geometric structure between images, resulting in

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  • Viewpoint tracking method based on geometrical reconstruction and semantic integration
  • Viewpoint tracking method based on geometrical reconstruction and semantic integration
  • Viewpoint tracking method based on geometrical reconstruction and semantic integration

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Embodiment Construction

[0068] In this embodiment, a viewpoint tracking method based on geometric reconstruction and semantic fusion, such as figure 1 As shown, follow the steps below:

[0069] Step 1. Construct the visual feature set of the sub-image: this stage is mainly to obtain the visual feature matrix containing color features, texture features and geometric structure features of the source image;

[0070] Step 1.1, use the clustering method to divide the source image into l sub-regions, use each sub-region as a node, construct a sub-graph containing several nodes, and obtain the sub-graph set G={G 1 ,G 2 ,...,G n ,...,G N}, N represents the total number of subgraphs; G n Represents the nth subgraph in the subgraph set G, and has Indicates that the nth subgraph contains t n nodes; and means G n Middle t i nodes; E n means G n middle t n A set of geometric connection edges between nodes, and the connection edges must ensure that G n It is a connected graph with the number of ...

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Abstract

The invention discloses a viewpoint tracking method based on geometrical reconstruction and semantic integration. The viewpoint tracking method is characterized by comprising the steps of 1, establishing the visual characteristic set of subgraphs, 2, integrating semantic characteristics, 3, active learning algorithm, 4, optimization updating, and 5, ranking and orderly linking the subgraphs to obtain a viewpoint tracking path. The viewpoint tracking method is capable of quickly and accurately detecting various remarkable regions of images, improving the accuracy of viewpoint tracking and then improving the prediction capacity of a viewpoint transfer route.

Description

technical field [0001] The invention belongs to the technical fields of image cognitive reconstruction, image enhancement, and image classification, and mainly relates to a viewpoint tracking method based on geometric reconstruction and semantic fusion. Background technique [0002] Viewpoint tracking is an intelligent image analysis method. Its purpose is to quickly find the visual information that users are most interested in and explain complex scenes by imitating human vision transfer. It is one of the hot research topics in the field of computer vision. Viewpoint tracking can be applied to image understanding, image compression, image classification, image redirection, information retrieval, etc. [0003] With the development of modern sensing technology and information processing technology, viewpoint tracking technology has also been greatly developed, but it still faces the following problems: [0004] One: In the process of tracking, the viewpoint often faces incom...

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Application Information

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IPC IPC(8): G06F17/30G06T7/60G06K9/46
CPCG06F16/35G06T7/60G06V10/56G06V10/462
Inventor 汪萌张鹿鸣郭丹田绪婷
Owner HEFEI UNIV OF TECH
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