An HMM-SVM-based automatic image annotation approach

Abstract

This paper presents a novel approach to Automatic Image Annotation (AIA) which combines both Hidden Markov Model (HMM) and Support Vector Machine (SVM). Typical image annotation methods directly map low-level features to high-level concepts and overlook the importance to mining the contextual information among the annotated keywords. The proposed HMM-SVM based approach comprises two different kinds of HMMs based on image color and texture features as the first-stage mapping scheme and an SVM which is based on the prediction results from the two HMMs as a so-called high-level classifier for final keywording. Our proposed approach assigns 1-5 keywords to each testing image. Using the Corel image dataset, Our experiments have shown that the combination of a discriminative classification and a generative model is beneficial in image annotation © 2011 Springer-Verlag Berlin Heidelberg.