Image Annotation Using Adapted Gaussian Mixture Model

In this paper, an automatic image annotation (AIA) method using Gaussian mixture model (GMM) is discussed. Supervised multiclass labeling (SML), which is a notable AIA method using GMM, has a problem of low annotation performances of labels that have a few training samples because of over fitting. In the present study, we propose to introduce a cross entropy based constraint into SML. According to the proposed method, while probabilistic models of labels are trained independently as is the case with SML, the optimization of whole probabilistic models is achieved, and therefore over fitting is suppressed. As the result of extensive evaluation tests, the proposed method obtained the best annotation performance in existing parametric methods of AIA.