A representation and classification method of high-order and high-dimensional image data

Abstract

The invention discloses a representation and classification method of high-order and high-dimensional image data, belonging to the field of pattern recognition, which solves the problems of noise influence in the process of image recognition, destruction of original image structure, high data feature dimension, incompatibility between expression algorithm and classification algorithm and the like.The technical scheme adopted by the invention is that the image data is projected from the original space into the low-rank subspace according to a pattern by using a projection matrix set to obtaina low-rank representation of the data; a sparse representation dictionary and a linear classifier are trained in the low rank subspace to classify the image data. The image data is projected from theoriginal space to the low rank subspace by using the projection matrix set, and the low rank representation of the image data is obtained. Sparse representation dictionaries and linear classifiers aretrained in low rank subspaces to classify image data.

Description

technical field [0001] The invention belongs to the field of pattern recognition, in particular to a representation and classification method of high-order high-dimensional image data. Background technique [0002] With the development of human society, a large amount of image data is collected every day, and intelligent processing and analysis of image data has become a research hotspot. The purpose of image recognition is to replace human beings with computers to complete the recognition task of image data. Image representation and classification are the focus and difficulty of image recognition research. [0003] Image representation is further divided into image preprocessing and image feature extraction. Image preprocessing includes eliminating noise and distortion, repairing missing information, and image segmentation; image feature extraction is based on task requirements, using dimensionality reduction algorithms and image processing techniques to extract more stre...

AI Technical Summary

Problems solved by technology

[0006] 1) Errors often occur in the process of image acquisition, transmission and storage, resulting in data being polluted and distorted by noise, or even damaged and missing, which is especially common in high-order and high-dimensional data

[0007] 2) Traditional methods generally use vectors to represent images, and need to expand the collected images

This process not only destroys the original spatial structure of the image, loses the correlation information between pixels, but also increases the data dimension, which is easy to cause dimension disaster and small sample problem.

[0008] The representation algorithm and the classification algorithm are often designed independently, and there are often situations where the representation algorithm and the classification algorithm do not match

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