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Semantic segmentation is an interesting and challenging problem in computer vision, which aims to assign each superpixel in an image to one of pre-defined semantic categories. The key problem of semantic segmentation is to learn superpixel classifiers which identify semantic label for each superpixel in images. In traditional fully supervised methods, various machine learning techniques are used to train classifiers on labeled superpixels; however, in many practical applications, it is not easy to obtain enough labeled superpixels to learn satisfying classifiers for semantic segmentation. On the contrary, only image-level labels are necessary in weakly supervised semantic segmentation. We perform semantic segmentation in weak supervision by two approaches: I) We try to estimate the superpixel labels in the training set based on image-level labels such that superpixel classifiers can be trained. II) Alternatively, we select optimal classifier by parameters evaluation instead of training. More specifically, we firstly sample the classifier parameters at random and then evaluate the superpixel classifiers by measuring the reconstruction errors among the ground-truth negative samples and the predicted positive samples.
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