{"title":"图像相似度的贝叶斯网络方法","authors":"Y. Herdiyeni, Rizki Pebuardi, A. Buono","doi":"10.1109/ICICI-BME.2009.5417298","DOIUrl":null,"url":null,"abstract":"This paper proposed Bayesian Network approach for image similarity measurement based on color, shape and texture. Bayesian network model can determine dominant information of an image using occurrence probability of image's characteristics. This probability is used to measure image similarity. Performance of the system is determined using recall and precision. Based on experiment, Bayesian network model can improve performance of image retrieval system. Experiment result showed that the average precision gain up of using Bayesian network model is about 8.28 %. The average precision of using Bayesian network model is better than using color, shape, or texture information individually.","PeriodicalId":191194,"journal":{"name":"International Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Bayesian network approach for image similarity\",\"authors\":\"Y. Herdiyeni, Rizki Pebuardi, A. Buono\",\"doi\":\"10.1109/ICICI-BME.2009.5417298\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposed Bayesian Network approach for image similarity measurement based on color, shape and texture. Bayesian network model can determine dominant information of an image using occurrence probability of image's characteristics. This probability is used to measure image similarity. Performance of the system is determined using recall and precision. Based on experiment, Bayesian network model can improve performance of image retrieval system. Experiment result showed that the average precision gain up of using Bayesian network model is about 8.28 %. The average precision of using Bayesian network model is better than using color, shape, or texture information individually.\",\"PeriodicalId\":191194,\"journal\":{\"name\":\"International Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICI-BME.2009.5417298\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICI-BME.2009.5417298","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper proposed Bayesian Network approach for image similarity measurement based on color, shape and texture. Bayesian network model can determine dominant information of an image using occurrence probability of image's characteristics. This probability is used to measure image similarity. Performance of the system is determined using recall and precision. Based on experiment, Bayesian network model can improve performance of image retrieval system. Experiment result showed that the average precision gain up of using Bayesian network model is about 8.28 %. The average precision of using Bayesian network model is better than using color, shape, or texture information individually.