{"title":"基于广义Hebbian算法的图像压缩技术","authors":"Sagar Sachdeva, Vinay Kumar, V. Bawa","doi":"10.1109/ISPCC.2017.8269750","DOIUrl":null,"url":null,"abstract":"In this manuscript neural networks architecture is used for image compression. We analyzed the PCA technique with the help of neural networks architecture in which the synaptic weights act as the principal components which are trained through the Generalized Hebbian Algorithm (GHA). A comparison with traditional PCA is performed to demonstrate and illustrate the training and capabilities of the GHA for image compression.","PeriodicalId":142166,"journal":{"name":"2017 4th International Conference on Signal Processing, Computing and Control (ISPCC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generalized Hebbian Algorithm based technique for image compression\",\"authors\":\"Sagar Sachdeva, Vinay Kumar, V. Bawa\",\"doi\":\"10.1109/ISPCC.2017.8269750\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this manuscript neural networks architecture is used for image compression. We analyzed the PCA technique with the help of neural networks architecture in which the synaptic weights act as the principal components which are trained through the Generalized Hebbian Algorithm (GHA). A comparison with traditional PCA is performed to demonstrate and illustrate the training and capabilities of the GHA for image compression.\",\"PeriodicalId\":142166,\"journal\":{\"name\":\"2017 4th International Conference on Signal Processing, Computing and Control (ISPCC)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 4th International Conference on Signal Processing, Computing and Control (ISPCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPCC.2017.8269750\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 4th International Conference on Signal Processing, Computing and Control (ISPCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPCC.2017.8269750","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Generalized Hebbian Algorithm based technique for image compression
In this manuscript neural networks architecture is used for image compression. We analyzed the PCA technique with the help of neural networks architecture in which the synaptic weights act as the principal components which are trained through the Generalized Hebbian Algorithm (GHA). A comparison with traditional PCA is performed to demonstrate and illustrate the training and capabilities of the GHA for image compression.
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