We consider the task of learning the parameters of a segmentation model that assigns a specific semantic class to each pixel of a given image. The main problem we face is the lack of fully supervised data. We address this issue by developing a principled framework for learning the parameters of a specific-class segmentation model using diverse data. More precisely, we propose a latent structural support vector machine formulation, where the latent variables model any missing information in the human annotation. Of particular interest to us are three types of annotations: (i) images segmented using generic foreground or background classes; (ii) images with bounding boxes specified for objects; and (iii) images labeled to indicate the presence of a class. Using large, publicly available datasets we show that our approach is able to exploit the information present in different annotations to improve the accuracy of a stateof-the art region-based model.
In order to exploit both image-level and box-level labels, Kumar et al. REF propose a latent structural support vector machine (LSVM) formulation.
Learning specific-class segmentation from diverse data
2011 International Conference on Computer Vision
2011 International Conference on Computer Vision
Computer Science