Neural Network Based Text Detection in Videos Using Local Binary Patterns

被引：0

作者：

Ye, Jun ^{[1
]}

Huang, Lin-Lin ^{[1
]}

Hao, XiaoLi ^{[2
]}

机构：

[1] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China

[2] Beijing Jiaotong Univ, Beijing 100044, Peoples R China

来源：

PROCEEDINGS OF THE 2009 CHINESE CONFERENCE ON PATTERN RECOGNITION AND THE FIRST CJK JOINT WORKSHOP ON PATTERN RECOGNITION, VOLS 1 AND 2 | 2009年

关键词：

Video indexing; text detection; local binary patterns; polynomial neural network; FACE DETECTION; IMAGES; CLASSIFICATION; LOCALIZATION; EXTRACTION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The detection of texts in video images is an important task towards automatic content-based information indexing and retrieval system. In this paper, we propose a texture-based method for text detection in complex video images. Taking advantage of the desirable characteristic of gray-scale invariance of local binary patterns (LBP), we apply a modified LBP operator to extract feature of texts. A polynomial neural network (PNN) is employed to make classification. The PNN is trained with large quantities of samples collected using a bootstrap strategy. In addition, post-processing procedure including verification and integration is performed to refine the detected results. The effectiveness of the proposed method is demonstrated by experimental results.

引用

页码：916 / +

页数：2

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