Learning specific-class segmentation from diverse data

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 sp...

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Bibliographic Details
Main Authors: Kumar, M. P., Turki, H., Preston, D., Koller, D.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Computational modeling
Image segmentation
Inference algorithms
Labeling
Semantics
Training
Online Access:Request full text
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Description
Summary: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 state-of-the art region-based model.
ISSN:1550-5499
2380-7504
DOI:10.1109/ICCV.2011.6126446