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
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;
Conference_Titel :
Computational Intelligence and Design, 2009. ISCID '09. Second International Symposium on
Conference_Location :
Changsha
Print_ISBN :
978-0-7695-3865-5
DOI :
10.1109/ISCID.2009.284
