{"title":"Duplicate image detection in a stream of web visual data","authors":"Etienne Gadeski, H. Borgne, Adrian Daniel Popescu","doi":"10.1109/CBMI.2015.7153614","DOIUrl":null,"url":null,"abstract":"We consider the problem of indexing and searching image duplicates in streaming visual data. This task requires a fast image descriptor, a small memory footprint for each signature and a quick search algorithm. To this end, we propose a new descriptor satisfying the aforementioned requirements. We evaluate our method on two different datasets with the use of different sets of distractor images, leading to large-scale image collections (up to 85 million images). We compare our method to the state of the art and show it exhibits among the best detection performances but is much faster (one to two orders of magnitude).","PeriodicalId":387496,"journal":{"name":"2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMI.2015.7153614","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
We consider the problem of indexing and searching image duplicates in streaming visual data. This task requires a fast image descriptor, a small memory footprint for each signature and a quick search algorithm. To this end, we propose a new descriptor satisfying the aforementioned requirements. We evaluate our method on two different datasets with the use of different sets of distractor images, leading to large-scale image collections (up to 85 million images). We compare our method to the state of the art and show it exhibits among the best detection performances but is much faster (one to two orders of magnitude).