Discriminative Fusion Approach for Automatic Image Annotation

In this paper, two discriminative fusion schemes are proposed for automatic image annotation. One is the ensemble-pattern association based fusion and another is the model-based transformation. The fusion approaches are studied and evaluated in a unified framework for AIA based on the text representation of the image content and the MC MFoM learning. The schemes are flexible for fusing diverse visual features and multiple modalities. The discriminative learning can automatically weight the most important features for the classification. We evaluate the fusion schemes based on the Corel and TRECVID 2003 datasets. The experimental results clearly show that the proposed fusion schemes give a significant improvement in term of the mean of F₁ as well as the number of the detected concepts