{"title":"A Comparative Study of Feature Selection for SVM in Video Text Detection","authors":"Zhen Wang, Zhiqiang Wei","doi":"10.1109/ISCID.2009.284","DOIUrl":null,"url":null,"abstract":"In this paper, a comparative study with three support vector machines (SVM) classifiers was carried out. The input images were first preprocessed to form the candidate text string regions. Next, Based on different features sets extracted by different methods, three SVM classifiers are used to analyze the textural properties of text and classify the text and no text strings in video frames. Then, a comparative evaluation of their performance is presented. The goal of the paper is to identify good feature selection for SVM in video text detecting task.","PeriodicalId":294370,"journal":{"name":"International Symposium on Computational Intelligence and Design","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Symposium on Computational Intelligence and Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCID.2009.284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, a comparative study with three support vector machines (SVM) classifiers was carried out. The input images were first preprocessed to form the candidate text string regions. Next, Based on different features sets extracted by different methods, three SVM classifiers are used to analyze the textural properties of text and classify the text and no text strings in video frames. Then, a comparative evaluation of their performance is presented. The goal of the paper is to identify good feature selection for SVM in video text detecting task.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
