Improve Image Annotation by Using Markov Model

摘要：

In this paper, we propose a novel image annotation algorithm based on Markov model. This algorithm treats each candidate keyword as a state in Markov chain, and implements image annotation by estimating the probability of Markov transition. On one aspect, compared with classical algorithms, the proposed algorithm stops making the assumption that each keyword is independent to each other; instead, they are related to the existed keywords; On the other hand, not only considering correlation between keywords that improves results of image annotation, but our proposed approach also takes image visual content into account. 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 words correlation RPCL CMRM

作者: Jin-Shu Weng Zhong-Hua Sun Ke-Bin Jia

作者单位: Electronic Information & Control Engineering Beijing University of Technology Beijing,China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

会议地点: 大连

会议语种:英文

页码: 1354-1357

在线出版日期: 2010-07-05（万方平台首次上网日期，不代表论文的发表时间）