A New Algorithm for Image Annotation Based on Markov Model

摘要：

A novel image annotation algorithm based on Markov model is proposed to bridge the semantic gap of content-based image retrieval. This algorithm treats each candidate keyword as a state in Markov chain, and implements image annotation by estimating the probability of Markov transition. Compared with classical algorithms, the proposed algorithm consider correlation between keywords that improves results of image annotation, Experimental results on the typical Corel dataset demonstrate the effectiveness and the increasing annotation precision of our proposed algorithm.

关键词： Markov model transition probability RPCL words correlation CMRM

作者: Zhen An Zhonghua Sun Jinshu Weng Kebin Jia Jiang Li

作者单位: Beijing University of Technology, Beijing,China Patent Examination Cooperation Center of The Patent Office, Beijing,China

会议类型: 国际会议

会议名称: 2012 International Conference on Future Communication and Computer Technology(2012未来通信与计算机技术国际会议ICFCCT 2012)

会议地点: 哈尔滨

会议语种:英文

页码: 383-388

在线出版日期: 2012-05-19（万方平台首次上网日期，不代表论文的发表时间）