An Approach of Image Retrieval based on Bayesian and AAM

Semantic-based image retrieval using low-level visual features is a challenging and important issue in content-based image retrieval. In this paper, we cast the image retrieval issue in a Bayesian framework and AAM (the active appearance model). Specifically, we propose an approach for complex semantic-based image retrieval, for example selecting the grassland images including horses. That is, the approach is used for selecting images including specific scene and model. In the approach, we integrate low-level features and spatial distribution into Bayesian frame. The approach uses Bayesian framework to select the images including the scene (forest, grassland), and uses AAM to select the images including the specific model (horse). Experimental results indicate that our approach is effective in complex semantic-based image retrieval and provides a sound retrieval performance.