{"title":"多媒体应用的网络图像搜索引擎的比较评价","authors":"Keon Stevenson, C. Leung","doi":"10.1109/ICME.2005.1521641","DOIUrl":null,"url":null,"abstract":"While text-oriented document searching are relatively mature on the Internet, image searching, which requires much more than text matching, significantly lags behind. The use of image search engines significantly enlarges the scope of images to users accessibility. This paper provides an understanding of current technologies in image searching on the Internet, and points to future areas of improvement for multimedia applications. We develop a systematic set of image queries to assess the competence and performance of the major image search engines. We find that current technology is only able to deliver an average precision of around 42% and an average recall of around 12%, while the best performers are capable of producing over 70% for precision and around 27% for recall. The reasons for such differences, and mechanisms for search improvement, are also indicated.","PeriodicalId":244360,"journal":{"name":"2005 IEEE International Conference on Multimedia and Expo","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":"{\"title\":\"Comparative evaluation of Web image search engines for multimedia applications\",\"authors\":\"Keon Stevenson, C. Leung\",\"doi\":\"10.1109/ICME.2005.1521641\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"While text-oriented document searching are relatively mature on the Internet, image searching, which requires much more than text matching, significantly lags behind. The use of image search engines significantly enlarges the scope of images to users accessibility. This paper provides an understanding of current technologies in image searching on the Internet, and points to future areas of improvement for multimedia applications. We develop a systematic set of image queries to assess the competence and performance of the major image search engines. We find that current technology is only able to deliver an average precision of around 42% and an average recall of around 12%, while the best performers are capable of producing over 70% for precision and around 27% for recall. The reasons for such differences, and mechanisms for search improvement, are also indicated.\",\"PeriodicalId\":244360,\"journal\":{\"name\":\"2005 IEEE International Conference on Multimedia and Expo\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"31\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 IEEE International Conference on Multimedia and Expo\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICME.2005.1521641\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 IEEE International Conference on Multimedia and Expo","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICME.2005.1521641","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparative evaluation of Web image search engines for multimedia applications
While text-oriented document searching are relatively mature on the Internet, image searching, which requires much more than text matching, significantly lags behind. The use of image search engines significantly enlarges the scope of images to users accessibility. This paper provides an understanding of current technologies in image searching on the Internet, and points to future areas of improvement for multimedia applications. We develop a systematic set of image queries to assess the competence and performance of the major image search engines. We find that current technology is only able to deliver an average precision of around 42% and an average recall of around 12%, while the best performers are capable of producing over 70% for precision and around 27% for recall. The reasons for such differences, and mechanisms for search improvement, are also indicated.