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.
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Acknowledgments
This research was supported by Program of International science and technology cooperation (2015DFA10940); Science and technology support program (R&D) project of Hubei Province (2015BAA115); PhD Research Startup Foundation of Hubei University of Technology (No. BSQD13037, No. BSQD14028); Open Foundation of Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy (HBSKFZD2015005, HBSKFTD2016002).
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Wang, J., Shi, H., Liu, M., Xiong, W., Cheng, K., Jiang, Y. (2018). Image Semantic Segmentation Algorithm Based on Self-learning Super-Pixel Feature Extraction. In: Barolli, L., Xhafa, F., Javaid, N., Spaho, E., Kolici, V. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-75928-9_69
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DOI: https://doi.org/10.1007/978-3-319-75928-9_69
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