Image Recognition of Pledges of Capital Stock in Small- and Medium-Sized Enterprises Based on Partial Differential Equations

Dehui Zhou,Miaochao Chen

doi:10.1155/2021/6548344

Abstract

Image recognition is one of the core research directions in the field of computer vision research, which can be divided into general image recognition and fine-grained image recognition. General image recognition refers to the recognition of different types of objects; fine-grained image recognition refers to the recognition of different subclasses in the same broad class of objects, such as SME financing inventory pledge image recognition. In this paper, we propose a partial differential equation-based image recognition method for SME financing inventory pledges and conduct detailed analysis and experiments. Compared with general images, partial differential equation-based SME financing inventory pledges image recognition is difficult to recognize due to data characteristics such as small differences in features between classes, large differences in features within classes, and a small percentage of targets in the image. To address the problem that existing methods ignore the role of shallow features on fine-grained image recognition, this paper proposes a fine-grained image recognition method based on partial differential equations. By analyzing the important role of shallow features for fine-grained image recognition, a feature fusion method with adaptive weights is proposed. Using this method to fuse shallow and high-level semantic features for recognition, the role of shallow features in fine-grained image recognition is fully exploited. In addition, the proposed method does not change the order of magnitude of the model parameters and is highly transferable. The relevant experimental results verify the effectiveness of the proposed method.

Highlights

The invention of electronic computers during the third industrial revolution led to the information age
We will use simulation experiments of image recognition to test the effectiveness of image recognition methods based on primary classification method and secondary classification-based image recognition methods, respectively [19], record the relevant experimental results, analyze and compare the experimental results, and select the image recognition method with the best recognition effect as the method for image recognition of shelf goods
To verify the effectiveness of the classification recognition method, firstly, a single classification method mean distance method and error correction SVM are combined with the principal component analysis method PCA and linear discriminant analysis LDA, respectively, and the images in the commodity library of this paper are recognized under different feature dimensions, and the following are the experimental results

Summary

Introduction

The invention of electronic computers during the third industrial revolution led to the information age. The global Internet alone can generate petabytes of data of order of magnitude per day in terms of visual information volume [1]. This data contains a wealth of information that is useful for scientific research and for life. Effective use of this visual information is an important research component for industry and academia. Human beings want to process these visual data with the help of computers, forming the field of computer vision research. Computer vision has a wide range of application prospects in medical, transportation, security, science, agriculture, military, and other fields, mainly containing image recognition, target detection, target tracking, and image generation, and other research subdirections, of which image recognition is the basis for other tasks

Methods

Results

Conclusion