{"title":"层次重叠增长的神经气体网络在视频镜头检测和运动表征中的应用","authors":"Xiang Cao, P. N. Suganthan","doi":"10.1109/IJCNN.2002.1007642","DOIUrl":null,"url":null,"abstract":"This paper describes a hierarchical overlapped architecture (HOGNG) based upon the growing neural gas (GNG) network. The proposed architecture combines the unsupervised and supervised learning schemes in GNG. This novel network model was used to perform automatic video shot detection and motion characterization. Experimental results are presented to show the good classification accuracy of the proposed algorithm on real MPEG video sequences.","PeriodicalId":382771,"journal":{"name":"Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Hierarchical overlapped growing neural gas networks with applications to video shot detection and motion characterization\",\"authors\":\"Xiang Cao, P. N. Suganthan\",\"doi\":\"10.1109/IJCNN.2002.1007642\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a hierarchical overlapped architecture (HOGNG) based upon the growing neural gas (GNG) network. The proposed architecture combines the unsupervised and supervised learning schemes in GNG. This novel network model was used to perform automatic video shot detection and motion characterization. Experimental results are presented to show the good classification accuracy of the proposed algorithm on real MPEG video sequences.\",\"PeriodicalId\":382771,\"journal\":{\"name\":\"Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.2002.1007642\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2002.1007642","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hierarchical overlapped growing neural gas networks with applications to video shot detection and motion characterization
This paper describes a hierarchical overlapped architecture (HOGNG) based upon the growing neural gas (GNG) network. The proposed architecture combines the unsupervised and supervised learning schemes in GNG. This novel network model was used to perform automatic video shot detection and motion characterization. Experimental results are presented to show the good classification accuracy of the proposed algorithm on real MPEG video sequences.
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