Improve image annotation by using Markov model

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.