Dictionary learning based superpixels clustering for weakly-supervised semantic segmentation
Peng Ying, Jing Liu, Hanqing Lu · 2015
The task of weakly-supervised semantic segmentation is solved by assigning image-level labels to over-segmented superpixels. Considering that superpixels are geometrically and semantically ambiguous for label assignment, we propose a joint solution of semantic segmentation to enhance the learnability of superpixels. First, our model includes a spectral clustering item and a discriminative clustering item to obtain some clustering subsets of superpixels (ideally semantic regions), which are more separable semantically than independent superpixels. Second, sparse coding based feature for superpixel is adopted to make the representation robust to noise, and the dictionary for the sparse representation is learned together with the above clustering items. Third, a weakly supervised item for superpixels, transferred from image-level labels, is attached. We jointly formulate the above problems as a non-convex objective function, and optimize it by the constraint concave-convex programming (CCCP) algorithm. Extensive experiments on MSRC-21 and LabelMe datasets prove the effectiveness of our approach.