Weakly supervised discovery of common visual struc ture in highly variable, cluttered images is a key problem in recognition. We address this problem using deformable part-based models (DPM's) with latent SVM training [6] . These models have been introduced for fully supervised training of object detectors, but we demonstrate that they are also capable of more open-ended learning of latent structure for such tasks as scene recognition and weakly su pervised object localization. For scene recognition, DPM's can capture recurring visual elements and salient objects; in combination with standard global image features, they obtain state-of-the-art results on the MIT 67-category in door scene dataset. For weakly supervised object localiza tion, optimization over latent DPM parameters can discover the spatial extent of objects in cluttered training images without ground-truth bounding boxes. The resulting method outpeiforms a recent state-of-the-art weakly supervised ob ject localization approach on the PASCAL-07 dataset.
In REF , a deformable part-based model (DPM) has been applied to address scene categorization.
Scene recognition and weakly supervised object localization with deformable part-based models
2011 International Conference on Computer Vision
2011 International Conference on Computer Vision
Computer Science