{"title":"基于四叉树的图像表示与检索","authors":"Maude Manouvrier, M. Rukoz, G. Jomier","doi":"10.4018/978-1-59140-387-6.CH004","DOIUrl":null,"url":null,"abstract":"This chapter is a survey of quadtree uses in the image domain from image representation, to image storage and content-based retrieval. A quadtree is a spatial data structure built by a recursive decomposition of space into quadrants. Applied to images, it allows representing image content, compacting or compressing image information, and querying images. For thirteen years, numerous image-based approaches have used this structure. In this chapter, the authors want to underline the contribution of quadtree in image applications.","PeriodicalId":189216,"journal":{"name":"Spatial Databases","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Quadtree-Based Image Representation and Retrieval\",\"authors\":\"Maude Manouvrier, M. Rukoz, G. Jomier\",\"doi\":\"10.4018/978-1-59140-387-6.CH004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This chapter is a survey of quadtree uses in the image domain from image representation, to image storage and content-based retrieval. A quadtree is a spatial data structure built by a recursive decomposition of space into quadrants. Applied to images, it allows representing image content, compacting or compressing image information, and querying images. For thirteen years, numerous image-based approaches have used this structure. In this chapter, the authors want to underline the contribution of quadtree in image applications.\",\"PeriodicalId\":189216,\"journal\":{\"name\":\"Spatial Databases\",\"volume\":\"94 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Spatial Databases\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/978-1-59140-387-6.CH004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spatial Databases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-59140-387-6.CH004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This chapter is a survey of quadtree uses in the image domain from image representation, to image storage and content-based retrieval. A quadtree is a spatial data structure built by a recursive decomposition of space into quadrants. Applied to images, it allows representing image content, compacting or compressing image information, and querying images. For thirteen years, numerous image-based approaches have used this structure. In this chapter, the authors want to underline the contribution of quadtree in image applications.