{"title":"基于小波域多态隐马尔可夫模型的文档分割","authors":"Jin-ping Song, Xiaoyi Yang, Yuhua Hou, Y.Y. Tang","doi":"10.1109/ICMLC.2002.1174532","DOIUrl":null,"url":null,"abstract":"Presents a document segmentation algorithm, called context-adapted wavelet-domain hidden Markov tree (CAHMT) model, which extends the wavelet-domain hidden Markov tree (HMT) model. The proposed CAHMT can achieve more accurate quality with low computation complexity in document segmentation. In addition to further improving the segmenting performance, we combine a differential operator and the lowest frequency subband with CAHMT and produce much better visual segmentation quality than the HMT.","PeriodicalId":90702,"journal":{"name":"Proceedings. International Conference on Machine Learning and Cybernetics","volume":"15 1","pages":"991-994 vol.2"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Document segmentation using wavelet-domain multi-state hidden Markov models\",\"authors\":\"Jin-ping Song, Xiaoyi Yang, Yuhua Hou, Y.Y. Tang\",\"doi\":\"10.1109/ICMLC.2002.1174532\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Presents a document segmentation algorithm, called context-adapted wavelet-domain hidden Markov tree (CAHMT) model, which extends the wavelet-domain hidden Markov tree (HMT) model. The proposed CAHMT can achieve more accurate quality with low computation complexity in document segmentation. In addition to further improving the segmenting performance, we combine a differential operator and the lowest frequency subband with CAHMT and produce much better visual segmentation quality than the HMT.\",\"PeriodicalId\":90702,\"journal\":{\"name\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"volume\":\"15 1\",\"pages\":\"991-994 vol.2\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2002.1174532\",\"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. International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2002.1174532","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Document segmentation using wavelet-domain multi-state hidden Markov models
Presents a document segmentation algorithm, called context-adapted wavelet-domain hidden Markov tree (CAHMT) model, which extends the wavelet-domain hidden Markov tree (HMT) model. The proposed CAHMT can achieve more accurate quality with low computation complexity in document segmentation. In addition to further improving the segmenting performance, we combine a differential operator and the lowest frequency subband with CAHMT and produce much better visual segmentation quality than the HMT.
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