Nuclear norm regularization based low-rank image characteristic extraction identification method and system

Abstract

The invention discloses a nuclear norm regularization based low-rank image characteristic extraction identification method and system. Firstly an original training image is subjected to similarity learning to construct a reconstruction weight coefficient; and secondly a nuclear norm measurement based neighborhood reconstruction error is minimized and a projection matrix is subjected to nuclear norm regularization processing to obtain a low-rank projection matrix capable of directly extracting two-dimensional image characteristics, so that the topological structure and correlativity among image pixels can be effectively kept. In addition, it can be ensured that low-rank salient image characteristics are obtained by optimization. An original test image is directly embedded in the low-rank projection matrix obtained by training, low-rank salient characteristics of the image are output, classification is performed by utilizing a nearest neighbor classifier based on low-rank salient characteristics in a training set, and category labels of training image samples with highest characteristic similarity with test image samples are obtained, thereby finishing the classification of the test image samples. By introducing nuclear norm regularization, the robustness of noises in a characteristic extraction process can be effectively ensured and the system performance is better.

Description

technical field [0001] The invention relates to the technical field of computer vision and image recognition, in particular to a recognition method and system for extracting low-rank image features based on nuclear norm regularization. Background technique [0002] With the rapid development of the information age, the importance of efficient processing of data and information in scientific development is increasing day by day. As a data preprocessing step, feature extraction technology plays an important role in the research of computer vision, data mining and image processing and other fields. Feature extraction is a technique that effectively preserves the geometric structure of the original data while embedding given high-dimensional data into a low-dimensional representation space. This technology is of great significance in machine vision systems and image processing. In practical applications, the social and economic benefits it produces are immeasurable. However, i...

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Problems solved by technology

[0005] In view of this, the purpose of the present invention is to provide a recognition method and system based on nuclear norm regularization for low-rank image feature extraction, which overcomes the problems that existing models are very sensitive to noise and heterogeneous data, and improves the performance of the algorithm at the same time. Robustness, realizing a more effective image feature extraction scheme

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