{"title":"一种新颖高效的基于CNN的花卉CBIR","authors":"Subash. S. I, Muthiah. M. A., N. Mathan","doi":"10.1109/WiSPNET57748.2023.10134508","DOIUrl":null,"url":null,"abstract":"Image processing is vital to extract the required data from images. Machine learning is an efficient tool used for penetration in most of the classification and identification tasks performed by a computer. This project proposes the identification of a flower after the classification of flower images using a successful artificial intelligence tool named the Convolutional Neural Network (CNN). Models similar to this project have been used in most search engines for a long time, but CBIR (content-based image retrieval) still runs with less accuracy and produces outputs with fewer specifications due to the use of convolutional feed-forward networks for image retrieval. System performance depends a lot on the drawn-out features extracted from images. So, it is required to develop a CBIR system that retrieves similar images without explicit feature extraction and classification by using CNN, which accepts images as input. For experimentation, images from the Oxford-102 flower dataset are used.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel and Efficient CBIR using CNN for Flowers\",\"authors\":\"Subash. S. I, Muthiah. M. A., N. Mathan\",\"doi\":\"10.1109/WiSPNET57748.2023.10134508\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image processing is vital to extract the required data from images. Machine learning is an efficient tool used for penetration in most of the classification and identification tasks performed by a computer. This project proposes the identification of a flower after the classification of flower images using a successful artificial intelligence tool named the Convolutional Neural Network (CNN). Models similar to this project have been used in most search engines for a long time, but CBIR (content-based image retrieval) still runs with less accuracy and produces outputs with fewer specifications due to the use of convolutional feed-forward networks for image retrieval. System performance depends a lot on the drawn-out features extracted from images. So, it is required to develop a CBIR system that retrieves similar images without explicit feature extraction and classification by using CNN, which accepts images as input. For experimentation, images from the Oxford-102 flower dataset are used.\",\"PeriodicalId\":150576,\"journal\":{\"name\":\"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WiSPNET57748.2023.10134508\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WiSPNET57748.2023.10134508","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image processing is vital to extract the required data from images. Machine learning is an efficient tool used for penetration in most of the classification and identification tasks performed by a computer. This project proposes the identification of a flower after the classification of flower images using a successful artificial intelligence tool named the Convolutional Neural Network (CNN). Models similar to this project have been used in most search engines for a long time, but CBIR (content-based image retrieval) still runs with less accuracy and produces outputs with fewer specifications due to the use of convolutional feed-forward networks for image retrieval. System performance depends a lot on the drawn-out features extracted from images. So, it is required to develop a CBIR system that retrieves similar images without explicit feature extraction and classification by using CNN, which accepts images as input. For experimentation, images from the Oxford-102 flower dataset are used.