Automatic images annotation extension using a probabilistic graphical model

Abstract

With the fast development of digital cameras and social media image sharing, automatic image annotation has become a research area of great interest. It enables indexing, extracting and searching in large collections of images in an easier and faster way. In this paper, we propose a model for the annotation extension of images using a probabilistic graphical model. This model is based on a mixture of multinomial distributions and mixtures of Gaussians. The results of the proposed model are promising on three standard datasets: Corel-5k, ESP-Game and IAPRTC-12.