Semantic Image Segmentation Method with Multiple Adjacency Trees and Multiscale Features

Semantic image segmentation is the basis of image understanding, which is one of the most important human cognitive activities. Cognitive studies have shown that human neocortical information transmission depends on cognitive processing at multiple scales, and contextual information aids the human cognitive system in solving perceptual inference tasks. Inspired by multiscale cognitive mechanisms and contextual effects, in this paper, we propose a semantic image segmentation method addressing multiscale features and contextual information. To integrate multiscale features, after over-segmenting an image into small-scale segments, we employ a segment-based classifier and a CRF (conditional random field) model to generate large-scale regions. We then use the features of regions to train a region-based classifier. To capture context, we propose a multiple adjacency tree model where each tree represents one type of region relevance and can be generated by the adjacency graph corresponding to that relevance metric. Using the multiple tree model instead of a general graph model, we can perform exact inference with some simple assumptions and capture multiple types of regional context dependency. Experiments on the MSRC-21 and Stanford background datasets show advantages of our method over a segment-based CRF model using single-scale features. The results demonstrate the importance of multiscale features and contextual information.