Image semantic segmentation algorithm based on self-learning super-pixel feature extraction

Abstract

Image semantic segmentation is a challenging task, influenced by high segmentation complexity, increased feature space sparseness and the semantic expression inaccurate. This paper proposes a stacked deconvolution neural network (SDN) based on adaptive super-pixel feature extraction to degrade computational cost and improve segmentation effectiveness. Firstly, the super-pixel segmentation is accomplished by simple linear iterative cluster (SLIC). Secondly, we add texture information as an optimization information to the evaluation function to guide the super-pixel segmentation and ensure the integrity of the super-pixel segmentation. Finally, we train a Stacked Deconvolution Neural Network (SDN) on the ISPRS Potsdam and the NZAM/ONERA Christchurch datasets and learn the sample data with weak annotation information to realize the accurate and fast super-pixel segmentation. Segmentation tests show that the proposed method can achieve the accurate segmentation of image semantics.