John R. Smith, D. Doermann, Amarnath Gupta, J. Goldstein, U. Shaft, N. Ratha
{"title":"多媒体应用:超越相似性搜索","authors":"John R. Smith, D. Doermann, Amarnath Gupta, J. Goldstein, U. Shaft, N. Ratha","doi":"10.1145/1160939.1160957","DOIUrl":null,"url":null,"abstract":"Relational database systems solve many of the traditional problems for processing of structured data. However, unstructured data in the form of images, video, audio and multimedia is growing at a tremendous rate and introduces new requirements that are not met by today's database engines. One well known example is content-based retrieval that involves similarity searching and indexing in high-dimensional feature spaces. In addition there has been much recent focus on applying machine learning techniques involving semantics modeling, spatio-temporal indexing, multi-modal (audio-, visual-, textual-) integration and relevance feedback searching.","PeriodicalId":346313,"journal":{"name":"Computer Vision meets Databases","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multimedia applications: beyond similarity searches\",\"authors\":\"John R. Smith, D. Doermann, Amarnath Gupta, J. Goldstein, U. Shaft, N. Ratha\",\"doi\":\"10.1145/1160939.1160957\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Relational database systems solve many of the traditional problems for processing of structured data. However, unstructured data in the form of images, video, audio and multimedia is growing at a tremendous rate and introduces new requirements that are not met by today's database engines. One well known example is content-based retrieval that involves similarity searching and indexing in high-dimensional feature spaces. In addition there has been much recent focus on applying machine learning techniques involving semantics modeling, spatio-temporal indexing, multi-modal (audio-, visual-, textual-) integration and relevance feedback searching.\",\"PeriodicalId\":346313,\"journal\":{\"name\":\"Computer Vision meets Databases\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Vision meets Databases\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1160939.1160957\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision meets Databases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1160939.1160957","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Relational database systems solve many of the traditional problems for processing of structured data. However, unstructured data in the form of images, video, audio and multimedia is growing at a tremendous rate and introduces new requirements that are not met by today's database engines. One well known example is content-based retrieval that involves similarity searching and indexing in high-dimensional feature spaces. In addition there has been much recent focus on applying machine learning techniques involving semantics modeling, spatio-temporal indexing, multi-modal (audio-, visual-, textual-) integration and relevance feedback searching.