Quan Zhang, Xiaoying Tai, Yi-hong Dong, Shanliang Pan, Xin Luo, K. Kita
{"title":"基于二级内容的内窥镜图像检索","authors":"Quan Zhang, Xiaoying Tai, Yi-hong Dong, Shanliang Pan, Xin Luo, K. Kita","doi":"10.1109/ICNC.2008.502","DOIUrl":null,"url":null,"abstract":"Based on the analysis of endoscope image, in this paper, a new color quantification method is proposed to extract improved CCV and the V component shape invariant moment achieving image feature base. Inspiring from general information searching, the two-level content-based endoscope image retrieval is represented using the improved CCV and V component shape invariant moment guaranteeing the first retrieval recall. Experiments prove the efficiency of these methods.","PeriodicalId":6404,"journal":{"name":"2008 Fourth International Conference on Natural Computation","volume":"36 1","pages":"208-212"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Two-Level Content-Based Endoscope Image Retrieval\",\"authors\":\"Quan Zhang, Xiaoying Tai, Yi-hong Dong, Shanliang Pan, Xin Luo, K. Kita\",\"doi\":\"10.1109/ICNC.2008.502\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the analysis of endoscope image, in this paper, a new color quantification method is proposed to extract improved CCV and the V component shape invariant moment achieving image feature base. Inspiring from general information searching, the two-level content-based endoscope image retrieval is represented using the improved CCV and V component shape invariant moment guaranteeing the first retrieval recall. Experiments prove the efficiency of these methods.\",\"PeriodicalId\":6404,\"journal\":{\"name\":\"2008 Fourth International Conference on Natural Computation\",\"volume\":\"36 1\",\"pages\":\"208-212\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Fourth International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2008.502\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Fourth International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2008.502","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Based on the analysis of endoscope image, in this paper, a new color quantification method is proposed to extract improved CCV and the V component shape invariant moment achieving image feature base. Inspiring from general information searching, the two-level content-based endoscope image retrieval is represented using the improved CCV and V component shape invariant moment guaranteeing the first retrieval recall. Experiments prove the efficiency of these methods.
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