Resulted word counts optimization—A new approach for better automatic image annotation

One of major problems in image auto-annotation is the difference between the expected word counts vector and the resulted word counts vector. This paper presents a new approach to automatic image annotation—an algorithm called resulted word counts optimizer which is an extension to existing methods. An ideal annotator is defined in terms of recall quality measure. On the basis of the ideal annotator an optimization criterion is defined. It allows to reduce the difference between resulted and expected word counts vectors. The proposed algorithm can be used with various image auto-annotation algorithms because its generic nature. Additionally, it does not increase the computational complexity of the original annotation method processing phase. It changes output word probabilities according to a pre-calculated vector of correction coefficients.