Hierarchical Gaussian Mixture Model for Image Annotation via PLSA

In order to mimic the representation of textual documents, some approaches have recently been proposed to represent visual contents in terms of visual words in many applications such as object recognition and image annotation. In this paper, we propose to build an effective visual vocabulary by using Hierarchical Gaussian Mixture model instead of traditional clustering methods. In addition, Probabilistic Latent Semantic Analysis is employed to explore semantic aspects of visual concepts and to discover topic clusters among documents and visual words so that every image is projected on to a lower dimensional topic space for more efficient and effective annotation. Experimental results obtained on TRECVID 2005 dataset demonstrate that the Hierarchical Gaussian Mixture model can achieve better annotation performance than hierarchical k-means clustering even by using simple k-NN annotation scheme.