Using Maximum Entropy for Automatic Image Annotation

In this paper, we propose the use of the Maximum Entropy approach for the task of automatic image annotation. Given labeled training data, Maximum Entropy is a statistical technique which allows one to predict the probability of a label given test data. The techniques allow for relationships between features to be effectively captured. and has been successfully applied to a number of language tasks including machine translation. In our case, we view the image annotation task as one where a training data set of images labeled with keywords is provided and we need to automatically label the test images with keywords. To do this, we first represent the image using a language of visterms and then predict the probability of seeing an English word given the set of visterms forming the image. Maximum Entropy allows us to compute the probability and in addition allows for the relationships between visterms to be incorporated. The experimental results show that Maximum Entropy outperforms one of the classical translation models that has been applied to this task and the Cross Media Relevance Model. Since the Maximum Entropy model allows for the use of a large number of predicates to possibly increase performance even further, Maximum Entropy model is a promising model for the task of automatic image annotation.