Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Graph cut based inference with co-occurrence statistics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
An Efficient Approach to Semantic Segmentation
International Journal of Computer Vision
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Weakly supervised semantic segmentation with a multi-image model
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Salient Object Detection using concavity context
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Structured image segmentation using kernelized features
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Visual dictionary learning for joint object categorization and segmentation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Co-inference for multi-modal scene analysis
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
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We propose a novel approach to semantic segmentation using weakly supervised labels. In traditional fully supervised methods, superpixel labels are available for training; however, it is not easy to obtain enough labeled superpixels to learn a satisfying model for semantic segmentation. By contrast, only image-level labels are necessary in weakly supervised methods, which makes them more practical in real applications. In this paper we develop a new way of evaluating classification models for semantic segmentation given weekly supervised labels. For a certain category, provided the classification model parameter, we firstly learn the basis superpixels by sparse reconstruction, and then evaluate the parameters by measuring the reconstruction errors among negative and positive superpixels. Based on Gaussian Mixture Models, we use Iterative Merging Update (IMU) algorithm to obtain the best parameters for the classification models. Experimental results on two real-world datasets show that the proposed approach outperforms the existing weakly supervised methods, and it also competes with state-of-the-art fully supervised methods.