Semantic segmentation via sparse coding over hierarchical regions

The purpose of this paper is segmenting objects in an image and assigning a predefined semantic label to each object. There are two contributions in this paper. On one hand, semantic segmentation is guided by hierarchical regions instead of by single-level regions or multi-scale regions generated by multiple segmentations. On the other hand, sparse coding is introduced as high level description of the regions, which contributes to reduction of quantization error compared to traditional bag-of-visual-words method. Experiments on the challenging Microsoft Research Cambridge dataset (MSRC 21) show that our algorithm achieves state-of-the-art performance.