{"title":"使用尺度树分析和简化直方图","authors":"Stuart E. Gibson, Richard Harvey","doi":"10.1109/ICIAP.2001.956989","DOIUrl":null,"url":null,"abstract":"A new method for analysing image histograms is introduced. The technique decomposes a histogram into probability level sets. The relationships between these level sets are encoded using a tree. The tree has fewer nodes than the histogram and so is a compressed feature. When used in image retrieval experiments the tree is shown to have a performance that is superior to many methods and no worse than the best alternatives. The tree is efficient because it can be built using a computationally efficient algorithm known as a sieve.","PeriodicalId":365627,"journal":{"name":"Proceedings 11th International Conference on Image Analysis and Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Analysing and simplifying histograms using scale-trees\",\"authors\":\"Stuart E. Gibson, Richard Harvey\",\"doi\":\"10.1109/ICIAP.2001.956989\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new method for analysing image histograms is introduced. The technique decomposes a histogram into probability level sets. The relationships between these level sets are encoded using a tree. The tree has fewer nodes than the histogram and so is a compressed feature. When used in image retrieval experiments the tree is shown to have a performance that is superior to many methods and no worse than the best alternatives. The tree is efficient because it can be built using a computationally efficient algorithm known as a sieve.\",\"PeriodicalId\":365627,\"journal\":{\"name\":\"Proceedings 11th International Conference on Image Analysis and Processing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 11th International Conference on Image Analysis and Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIAP.2001.956989\",\"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 11th International Conference on Image Analysis and Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIAP.2001.956989","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysing and simplifying histograms using scale-trees
A new method for analysing image histograms is introduced. The technique decomposes a histogram into probability level sets. The relationships between these level sets are encoded using a tree. The tree has fewer nodes than the histogram and so is a compressed feature. When used in image retrieval experiments the tree is shown to have a performance that is superior to many methods and no worse than the best alternatives. The tree is efficient because it can be built using a computationally efficient algorithm known as a sieve.
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