Building façade semantic segmentation based on K-means classification and graph analysis
- 59 Downloads
Building façade segmentation is essential for smart city-related applications such as energy consumption simulation or urban planning. In this paper, we take advantage of the horizontal self-similarity feature of building texture and propose a building façade segmentation algorithm based on K-means classification. First, the building texture images are rectified to orthogonal projection. Then, texture pixels in each horizontal line are classified into line segments using the K-means method based on CIE color distance. Next, a graph is generated where the nodes represent line segments, and the edges are relatedly connected with color distance attribute of its two nodes. The connected nodes (neighbor line segments) with similar color are aggregated based on which the building main structures such as floors and tiles are detected. The novelty of the proposed method is that the K-means classification is applied to the building texture pixels in a horizontal line that can improve the classification accuracy and increase speed. According to the experimental results, the proposed algorithm can achieve over 90% accuracy on the test dataset compared with traditional methods.
KeywordsBuilding texture Semantic segmentation K-means Graph analysis
This research was funded by the National Natural Science Foundation of China (41671457) and the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (16KJA170003).
- Chaudhury K, DiVerdi S, Ioffe S (2014) Auto-rectification of user photos. 2014 IEEE International Conference on Image Processing (ICIP). IEEEGoogle Scholar
- Fathalla R, Vogiatzis G (2017) A deep learning pipeline for semantic facade segmentation. In: Proceedings of the British Machine Vision Conference 2016, BMVC 2017, September, 2017. British machine vision conference, 10/09/17Google Scholar
- Lian Y, Shen X (2015) Detecting repetitive elements with accurate locations and shapes from urban façade. 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, pp 1920–1924Google Scholar
- Liu H, Zhang J, Zhu J, Hoi S (2017) DeepFacade: a deep learning approach to facade parsing. Proceedings of the twenty-sixth international joint conference on artificial intelligence (IJCAI-17) 2301–2307Google Scholar
- Oskouie P, Becerik-Gerber B, Soibelman L (2017) Automated recognition of building açades for creation of as-is mock-up 3D models. J Comput Civ Eng 31:04017059. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000711 CrossRefGoogle Scholar
- Tyleček R, Šára R (2013) Spatial pattern templates for recognition of objects with regular structure. In: Weickert J, Hein M, Schiele B (eds) Pattern recognition. GCPR 2013. Lecture notes in computer science, vol 8142. Springer, BerlinGoogle Scholar
- Wang J, Liu C, Shen T, Quan L (2015) Structure-driven facade parsing with irregular patterns. 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, pp 041–045Google Scholar