In this work, we present a hybrid model (SVM-DMBRM) combining a generative and a discriminative model for the image annotation task. A support vector machine (SVM) is used as the discriminative model and a Discrete Multiple Bernoulli Relevance Model (DMBRM) is used as the generative model. The idea of combining both the models is to take advantage of the distinct capabilities of each model. The SVM tries to address the problem of poor annotation (images are not annotated with all relevant keywords), while the DMBRM model tries to address the problem of data imbalance (large variations in number of positive samples). Since DMBRM does not work well with high-dimensional data, a Latent Dirichlet Allocation (LDA) model is used to reduce the dimensionality of vector quantized features before using it. The hybrid model's results are comparable to or better than the state-of-the-art results on three standard datasets: Corel-5k, ESP-Game and IAPRTC-12.
Relevance model and support vector machine, generative and discriminative models respectively, are combined in REF .
A Hybrid Model for Automatic Image Annotation
ICMR
Computer Science