Image recognition and classification method of multi-classification support vector machine based on minimum reconstruction error search dimension reduction and particle swarm optimization

A technology of support vector machine and particle swarm optimization, applied in character and pattern recognition, computer components, instruments, etc., can solve problems such as computer recognition difficulties, increase fine search capabilities, solve problems with insufficient accuracy and increased diversity Effect

Pending Publication Date: 2020-11-17
JIANGSU UNIV
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AI Technical Summary

Problems solved by technology

Regardless of the resolution of the image itself, the data generated by it often has a high dimension, which brings great difficulties to the computer's recognition

Method used

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  • Image recognition and classification method of multi-classification support vector machine based on minimum reconstruction error search dimension reduction and particle swarm optimization
  • Image recognition and classification method of multi-classification support vector machine based on minimum reconstruction error search dimension reduction and particle swarm optimization
  • Image recognition and classification method of multi-classification support vector machine based on minimum reconstruction error search dimension reduction and particle swarm optimization

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Embodiment Construction

[0055] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when detailed descriptions of known functions and designs may dilute the main content of the present invention, these descriptions will be ignored here.

[0056] Algorithm train of thought of the present invention is:

[0057] Support Vector Machine (SVM) is a common binary classifier, aiming at minimizing structural risk, has good generalization ability and learning ability, and is widely used in signal classification, face recognition, text classification, garbage classification, etc. Mail filtering and other fields. When the support vector machine deals with multi-classification problems, it is necessary to realize the construction of multi-classifiers by combining multiple binary classifiers, often using one-to-many and ...

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Abstract

The invention provides an image recognition and classification method of a multi-classification support vector machine based on minimum reconstruction error search dimension reduction and particle swarm optimization which is used for image content recognition and classification. According to the method, firstly, optimal parameters of a dimension reduction algorithm are searched for through minimumreconstruction errors, then dimension reduction is conducted on samples in a handwritten digital data set through the optimized minimum reconstruction error search algorithm, key information is extracted from the samples, and the dimension is reduced to 17 dimensions. Secondly, a plurality of one-to-one support vector machine classifiers is constructed according to the number of sample categoriesin the data set, and the support vector machine classifiers are combined to indirectly realize multi-classification; and with the trained highest classification accuracy adopted as a target, global optimization is performed on the parameters of each support vector machine classifier by using a swarm intelligence optimization algorithm, so that a final image recognition and classification method with high recognition accuracy can be obtained.

Description

technical field [0001] The invention belongs to the technical field of computer application, and in particular relates to an image recognition and classification method based on minimum reconstruction error search KPCA image dimensionality reduction and multi-population PSO-SVMs classification. Background technique [0002] Human beings have a strong ability to recognize images. However, in today's society, in the context of a surge in data volume, all image recognition work is done by humans, which is not only time-consuming and labor-intensive, but also inefficient. With the rapid development of computer technology and pattern recognition, image recognition has gradually become an important technology in the information age. Various complex and precise mathematical models have emerged, and computers can therefore help humans process a large amount of physical information. [0003] In the field of pattern recognition, handwritten digit recognition is a very important part. ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/00
CPCG06N3/006G06F18/2411
Inventor 韩飞洪浩楠方升朱少钧彭禹铭
Owner JIANGSU UNIV
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