Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2013, Vol. 36 ›› Issue (3): 7-10,15.doi: 10.13190/jbupt.201303.6.zhaozhch

• Papers • Previous Articles     Next Articles

Moving Caption Detection Using Context and Relevance Vector Discriminant Analysis

ZHAO Zhi-cheng1,2, JIA Guo-li1, CAI An-ni1   

  1. 1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
    2. Beijing Key Laboratory of Network System and Network Culture, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2012-12-27 Online:2013-06-30 Published:2013-06-30

Abstract:

A moving caption detection method based on relevance vector machine (RVM) and the context of moving caption is proposed. Harris corner detector is used to determine caption region of video keyframes, and then the sparse optical flow field is obtained from Horn-Schunck(HS) optical flow algorithm, meanwhile, the motion and static text features is extracted respectively as well. A spatial-temporal context relationship among multiple text frames is described by features cascading. Finally, the relevance vector is learned and a two-class classifier is constructed. Experiments show that the performance of the proposed method is better than the existing four approaches, and supports vector machine-based algorithm.

Key words: corner, relevance vector machine, sparse optical flow, moving caption, context feature

CLC Number: