Sam S. Tsai, Huizhong Chen, David M. Chen, Vasu Parameswaran, R. Grzeszczuk, B. Girod
{"title":"Visual Text Features for Image Matching","authors":"Sam S. Tsai, Huizhong Chen, David M. Chen, Vasu Parameswaran, R. Grzeszczuk, B. Girod","doi":"10.1109/ISM.2012.84","DOIUrl":null,"url":null,"abstract":"We present a new class of visual text features that are based on text in camera phone images. A robust text detection algorithm locates individual text lines and feeds them to a recognition engine. From the recognized characters, we generate the visual text features in a way that resembles image features. We calculate their location, scale, orientation, and a descriptor that describes the character and word information. We apply visual text features to image matching. To disambiguate false matches, we developed a word-distance matching method. Our experiments with image that contain text show that the new visual text feature based image matching pipeline performs on par or better than a conventional image feature based pipeline while requiring less than 10 bits per feature. This is 4.5× smaller than state-of-the-art visual feature descriptors.","PeriodicalId":282528,"journal":{"name":"2012 IEEE International Symposium on Multimedia","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Symposium on Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISM.2012.84","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
We present a new class of visual text features that are based on text in camera phone images. A robust text detection algorithm locates individual text lines and feeds them to a recognition engine. From the recognized characters, we generate the visual text features in a way that resembles image features. We calculate their location, scale, orientation, and a descriptor that describes the character and word information. We apply visual text features to image matching. To disambiguate false matches, we developed a word-distance matching method. Our experiments with image that contain text show that the new visual text feature based image matching pipeline performs on par or better than a conventional image feature based pipeline while requiring less than 10 bits per feature. This is 4.5× smaller than state-of-the-art visual feature descriptors.