DocumentCode
2901866
Title
A Comparative Study of Feature Selection for SVM in Video Text Detection
Author
Zhen, Wang ; Zhiqiang, Wei
Author_Institution
Dept. of Comput. Sci., Ocean Univ. of China (OUC), Qingdao, China
Volume
2
fYear
2009
fDate
12-14 Dec. 2009
Firstpage
552
Lastpage
556
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.
Keywords
feature extraction; support vector machines; text analysis; video signal processing; SVM classifiers; feature selection; image preprocessing; support vector machines; text classification; text string regions; text strings; video frames; video text detection; Computational intelligence; Support vector machines; SVM classification; Text detection; feature selection; stroke-based; wavelet transform;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Design, 2009. ISCID '09. Second International Symposium on
Conference_Location
Changsha
Print_ISBN
978-0-7695-3865-5
Type
conf
DOI
10.1109/ISCID.2009.284
Filename
5368547
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