Integrating Low-level and Semantic Features for Object Consistent Segmentation

The aim of semantic segmentation is to assign each pixel a semantic label. Numerous methods for semantic segmentation have been proposed in recent years and most of them chose pixel or super pixel as the processing primitives. However, as the information contained in a pixel or a super pixel is not discriminative enough, the outputs of these algorithms are usually not object consistent. To tackle this problem, we introduce the concept of object-like regions as a new and higher level processing primitive. We first experimentally showed that using object-like regions as processing primitives can boost semantic segmentation accuracy, and then proposed a novel method to produce object-like regions by integrating state-of art low-level segmentation algorithms with typical semantic segmentation algorithms through a novel semantic feature feedback mechanism. We present experimental results on the publicly available image understanding database MSRC21 and show that the new method can achieve state of the art semantic segmentation results with far fewer processing primitives.