Image processing and classifying method and system

Abstract

The invention discloses an image processing and classifying method and system. The image processing method comprises the following steps: cutting main features of each sample image to obtain a plurality of local images containing the main features, and recording the local images as sub-images; combining the gray value vectors of the sub-pictures together to form a sample data matrix; constructinga weight coefficient representing the similarity degree between the pictures; determining a similarity matrix according to the weight coefficient; calculating a Laplace matrix; taking the minimum lossfunction as a target, and determining an optimal projection matrix according to the Laplace matrix and the sample data matrix; judging whether the loss function converges or not; and if not, updatingthe weight coefficient, and skipping to the step of determining the similar matrix, and if so, performing dimensionality reduction on the to-be-processed image by adopting the optimal projection matrix corresponding to the convergence of the loss function. According to the image processing method, the manifold structure embedded in the data can be reserved, and the image recognition method basedon the image processing method has the advantages of saving computing power and being high in recognition accuracy.

Description

technical field [0001] The invention relates to the technical field of image processing and pattern recognition, in particular to an image processing and classification method and system. Background technique [0002] In recent years, high-dimensional data has often appeared in related fields in the scientific and industrial circles, such as computer vision, pattern recognition, bioinformatics, and aerospace. When we deal with these data, their high-dimensional properties often become an obstacle to process and apply these data. Therefore, dimensionality reduction is an important technique to solve the analysis problems in the preprocessing stage of high-dimensional data. It plays an important role in related fields as a way to overcome the "curse of dimensionality". [0003] The purpose of dimensionality reduction is to embed high-dimensional data into a lower subspace while retaining the discriminative information of the data. In the past few decades, a large number of ...

AI Technical Summary

Problems solved by technology

The former is a supervised dimensionality reduction method that considers label information, while the latter is an unsupervised dimensionality reduction algorithm. However, these classic methods still have their shortcomings and defects.

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