A two-mode deep learning descriptor construction method based on graph primitives

Abstract

The present invention is applicable to the technical field of image registration, and provides a method for constructing a dual-mode deep learning descriptor based on graphics primitives. In this method, the attribute category of the patch image is learned by labeling samples, and the geometry of the patch image is learned by using the graphics primitive. Features, the attribute category and geometric features are fused to obtain the feature vector of the local patch image, that is, the descriptor based on the graphics primitive. The registration between patches is completed through the similarity of descriptor vectors, and the classification and description based on machine learning descriptors are realized. The dual-mode deep learning descriptor construction method based on graphics primitives proposed by the present invention is aimed at the classic image registration method CPU. Disadvantages of a large amount of calculation, explore the descriptor classification method of GPU (image processor) calculation. It mainly establishes a descriptor training set, builds a multi-mode convolutional network, trains categories and geometric modes on the GPU, and realizes classification and registration of local patch images. Solve the classification description method of the descriptor and the implementation problem on the GPU.

Description

technical field [0001] The invention belongs to the technical field of image registration, and provides a method for constructing a dual-mode deep learning descriptor based on image primitives. Background technique [0002] Feature matching is the mainstream method of image registration. The classic image registration method uses local feature descriptors, which take the local area of ​​the image centered on the key point as the object, and describe the features of the area according to the grayscale information of its internal pixels. , and a feature vector expressing the local information around the key points of the image is obtained. However, classical descriptors are computationally intensive and difficult to apply to real-time systems, and are not suitable for mobile devices. Contents of the invention [0003] An embodiment of the present invention provides a method for constructing a dual-mode deep learning descriptor based on an image primitive, which aims to solv...

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Problems solved by technology

[0003] The embodiment of the present invention provides a method for constructing a dual-mode deep learning descriptor based on image primitives, which aims to solve the problem that the classic descriptor has a large amount of calculation and is difficult to apply to a real-time system

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