Abstract
Image recognition is an important field of artificial intelligence. Its basic idea is to use computers to automatically classify different scenes in the acquired images, instead of traditional manual classification tasks. In this paper, through the analysis of rough set theory and artificial intelligence network, as well as the role of the two in image recognition, the rough set theory and artificial intelligence network are organically combined, and a network based on rough set theory and artificial intelligence network is proposed. Using BP artificial intelligence network model, improved BP artificial intelligence network model, and improved PSO-SVM model to identify and classify the extracted characteristic signals and compare the results, all reached 85% correct rate. The PCA and SVM are combined and applied to the MNIST handwritten digit collection for recognition and classification. At the data level, dimensionality reduction is performed on high-dimensional image data to compress the data. This greatly improves the performance of the algorithm, the recognition accuracy rate is as high as 98%, and the running time is shortened by about 90%. The model first preprocesses the original image data and then uses rough set theory to select features, which reduces the input dimension of the artificial intelligence network, improves the learning and recognition speed of the artificial intelligence network, and further improves the accuracy of recognition. The paper applies the model to handwritten digital image recognition, and the experimental results show that the model is effective and feasible. The system has the characteristics of easy deployment and easy maintenance and integration. Experiments show that the system has good time characteristics in the process of multialgorithm parallel image fusion processing.
Highlights
With the continuous development of the artificial intelligence era, the application of machine learning technology to speech recognition and image recognition has become two very important areas in pattern recognition [1]
If some of the features of these feature sets are removed, it will have a great impact on the recognition of the system. is impact is used to measure the importance of the feature to the recognition result
A big application of rough set theory is to simplify the observed data. e concepts of upper approximation set, lower approximation set, and kernel are used to extract useful features of the knowledge expression system and remove redundant features to achieve the purpose of simplification
Summary
Received 19 April 2021; Revised 31 May 2021; Accepted 8 June 2021; Published 16 June 2021. Using BP artificial intelligence network model, improved BP artificial intelligence network model, and improved PSOSVM model to identify and classify the extracted characteristic signals and compare the results, all reached 85% correct rate. Is greatly improves the performance of the algorithm, the recognition accuracy rate is as high as 98%, and the running time is shortened by about 90%. E model first preprocesses the original image data and uses rough set theory to select features, which reduces the input dimension of the artificial intelligence network, improves the learning and recognition speed of the artificial intelligence network, and further improves the accuracy of recognition. E paper applies the model to handwritten digital image recognition, and the experimental results show that the model is effective and feasible. Experiments show that the system has good time characteristics in the process of multialgorithm parallel image fusion processing
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