An HMM-SVM-based automatic image annotation approach

Authors:
Yinjie Lei;Wilson Wong;Wei Liu;Mohammed Bennamoun
Affiliations:
School of Computer Science and Software Engineering, University of Western Australia, Crawley WA;School of Computer Science and Software Engineering, University of Western Australia, Crawley WA;School of Computer Science and Software Engineering, University of Western Australia, Crawley WA;School of Computer Science and Software Engineering, University of Western Australia, Crawley WA
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Year:
2010

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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.