A novel region-based approach to visual concept modeling using web images

A novel region-based approach is proposed to model semantic concepts using web images. Web images are mined to obtain multiple visual patterns automatically that then are used to model a semantic concept. First, the salient region groups corresponding to the representative visual patterns of a concept are mined and selected as positive samples. Next, a representative visual pattern is built in each salient region group by using a BDA classifier. Finally all the visual patterns are aggregated to describe the concept by using a BDA ensemble approach. Because the proposed method models a semantic concept utilizing multiple visual patterns, it enhances the visual variability of a visual model when learning from diverse web images and improves the robustness of the visual model in handling segmentation-related uncertainties. Experiment results demonstrate our method performs well on generic images including not only "object" concepts, but also complex "scene" concepts.