{"title":"基于多层次神经网络的图像特征提取与分析算法","authors":"Erhui Xi","doi":"10.1109/ICCMC51019.2021.9418309","DOIUrl":null,"url":null,"abstract":"Image feature extraction and analysis algorithm based on multi-level neural network is studied in this paper. As a research direction of machine learning, deep learning method has been widely concerned. This method obtains more abstract and effective high-level semantic information by combining low-level features to discover different feature representations of data. This research work aims to design the model based on the multilevel neural network with the implementation of the feature extraction pipeline. The core of deep learning is feature learning, which aims to obtain the hierarchical feature information through a hierarchical network. The framework is validated through the image processing. The proposed algorithm is simulated on on the public database, and the result is efficient.","PeriodicalId":131747,"journal":{"name":"2021 5th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Image feature extraction and analysis algorithm based on multi-level neural network\",\"authors\":\"Erhui Xi\",\"doi\":\"10.1109/ICCMC51019.2021.9418309\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image feature extraction and analysis algorithm based on multi-level neural network is studied in this paper. As a research direction of machine learning, deep learning method has been widely concerned. This method obtains more abstract and effective high-level semantic information by combining low-level features to discover different feature representations of data. This research work aims to design the model based on the multilevel neural network with the implementation of the feature extraction pipeline. The core of deep learning is feature learning, which aims to obtain the hierarchical feature information through a hierarchical network. The framework is validated through the image processing. The proposed algorithm is simulated on on the public database, and the result is efficient.\",\"PeriodicalId\":131747,\"journal\":{\"name\":\"2021 5th International Conference on Computing Methodologies and Communication (ICCMC)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 5th International Conference on Computing Methodologies and Communication (ICCMC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCMC51019.2021.9418309\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 5th International Conference on Computing Methodologies and Communication (ICCMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCMC51019.2021.9418309","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image feature extraction and analysis algorithm based on multi-level neural network
Image feature extraction and analysis algorithm based on multi-level neural network is studied in this paper. As a research direction of machine learning, deep learning method has been widely concerned. This method obtains more abstract and effective high-level semantic information by combining low-level features to discover different feature representations of data. This research work aims to design the model based on the multilevel neural network with the implementation of the feature extraction pipeline. The core of deep learning is feature learning, which aims to obtain the hierarchical feature information through a hierarchical network. The framework is validated through the image processing. The proposed algorithm is simulated on on the public database, and the result is efficient.