{"type":"ScholarlyArticle","title":"Line separation from topographic maps using regional color and spatial information","authors":[{"name":"Pengfei Xu","slug":"pengfei-xu","given":"Pengfei","family":"Xu","affiliation":"School of Information Science and Technology , Northwest University , China"},{"name":"Qiguang Miao","slug":"qiguang-miao","given":"Qiguang","family":"Miao","affiliation":"School of Computer Science and Technology , Xidian University , China"},{"name":"Tiange Liu","slug":"tiange-liu","given":"Tiange","family":"Liu","affiliation":"School of information science and engineering , Yanshan University , China"},{"name":"Xiaojiang Chen","slug":"xiaojiang-chen","given":"Xiaojiang","family":"Chen","affiliation":"School of Information Science and Technology , Northwest University , China"},{"name":"Dingyi Fang","slug":"dingyi-fang","given":"Dingyi","family":"Fang","affiliation":"School of Information Science and Technology , Northwest University , China"}],"abstract":"The lines in topographic maps are difficult to be separated from each other because of their confusing colors. To solve this problem, we propose a novel line separation method using their regional color and spatial information. Firstly, we divide the lines into lots of circular regions with a certain diameter, and consider these regions as the basic processing units. Then based on a new concept of regional color confusion, we classify all the divided circular regions into two kinds of regions by whether the color is pure or mixed. Further, for pure color regions, a fuzzy clustering algorithm with Gaussian kernel can be used to cluster them into different lines based on their color information. Meanwhile, we determine the memberships of the mixed color regions according to their spatial relations with the clustered pure color regions. The concept of regional color confusion is proposed to reduce the influences of the confusing colors to line separation, and the spatial relations are utilized to solve the problems of the membership determination of the mixed color regions. The experimental results demonstrate that our method can achieve higher accuracy compare with other two state-of-the-art methods, which provides a novel idea for line element segmentation from scanned topographic maps.","doi":"10.24963/ijcai.2018/501","url":"https://ijcai.org/proceedings/2018/501","pdf":"https://ijcai.org/proceedings/2018/0501.pdf","markdown":"https://ijcai.org/proceedings/2018/501.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/501","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":501,"pages":{"first":3606,"last":3612},"keywords":["Constraints and SAT: Constraints: Applications","Constraints and SAT: Other approaches","Knowledge Representation and Reasoning: Information Fusion","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Chang et al., 2014a] Xiaojun Chang, Feiping Nie, Zhigang Ma, Yi Yang, and Xiaofang Zhou. A convex formulation for spectral shrunk clustering. pages 2532-2538, 2014.","title":"A convex formulation for spectral shrunk clustering","authors":"Chang","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chang et al., 2014b] Xiaojun Chang, Feiping Nie, and Sen Wang. Compound rank-k projections for bilinear anal- ysis. IEEE Transactions on Neural Networks Learning Systems, 27(7):1502-1513, 2014.","title":"Compound rank-k projections for bilinear analysis","authors":"Chang","year":"2014","venue":"IEEE Transactions on Neural Networks Learning Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2006] Yang Chen, Runsheng Wang, and Jing Qian. Extracting contour lines from common-conditioned topographic maps. IEEE Transactions on Geoscience & Remote Sensing, 44(4):1048-1057, 2006.","title":"Extracting contour lines from common-conditioned topographic maps","authors":"Yang Chen, Runsheng Wang, Jing Qian","year":"2006","venue":"IEEE Transactions on Geoscience & Remote Sensing","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2010] Hong Chen, Xiaoan Tang, Chenhao Wang, and Zhe Gan. Object oriented segmentation of scanned topographical maps. Journal of Image & Graph- ics, 15(9):1416-1424, 2010.","title":"Object oriented segmentation of scanned topographical maps","authors":"Chen","year":"2010","venue":"Journal of Image & Graphics","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chiang et al., 2009] Yao Yi Chiang, Craig A. Knoblock, Cyrus Shahabi, and Ching Chien Chen. Automatic and accurate extraction of road intersections from raster maps. Geoinformatica, 13(2):121-157, 2009.","title":"Automatic and accurate extraction of road intersections from raster maps","authors":"Yi Yao, Craig A Chiang, Cyrus Knoblock, Ching Shahabi, Chen Chien","year":"2009","venue":"Geoinformatica","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chiang et al., 2011] Yao Yi Chiang, Stefan Leyk, and Craig A. Knoblock. Efficient and robust graphics recogni- tion from historical maps. In International Conference on Graphics Recognition: New Trends and Challenges, pages 25-35, 2011.","title":"Efficient and robust graphics recognition from historical maps","authors":"Yi Yao, Stefan Chiang, Craig A Leyk, Knoblock","year":"2011","venue":"International Conference on Graphics Recognition: New Trends and Challenges","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chiang et al., 2014] Yao Yi Chiang, Stefan Leyk, and Craig A. Knoblock. A survey of digital map processing techniques. Acm Computing Surveys, 47(1):1-44, 2014.","title":"A survey of digital map processing techniques","authors":"Yi Yao, Stefan Chiang, Craig A Leyk, Knoblock","year":"2014","venue":"Acm Computing Surveys","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Gonzalez et al., 2014] Rafael C. Gonzalez, Richard E. Woods, and Steven L. Eddins. Digital image processing. Publishing House of Electronics Industry, 2014.","title":"Digital image processing","authors":"C Rafael, Richard E Gonzalez, Steven L Woods, Eddins","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Isik et al., 2014] Leyla Isik, Ethan M. Meyers, Joel Z. Leibo, and Tomaso Poggio. The dynamics of invariant object recognition in the human visual system. Journal of Neurophysiology, 111(1):91, 2014.","title":"The dynamics of invariant object recognition in the human visual system","authors":"Leyla Isik, Ethan M Meyers, Joel Z Leibo, Tomaso Poggio","year":"2014","venue":"Journal of Neurophysiology","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Khotanzad and Zink, 2003] Alireza Khotanzad and Ed- mund Zink. Contour line and geographic feature extrac- tion from usgs color topographical paper maps. Pattern Analysis & Machine Intelligence IEEE Transactions on, 25(1):18-31, 2003.","title":"Alireza Khotanzad and Edmund Zink. Contour line and geographic feature extraction from usgs color topographical paper maps","authors":"Zink Khotanzad","year":"2003","venue":"Pattern Analysis & Machine Intelligence IEEE Transactions on","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Levachkine et al., 2001] Serguei Levachkine, Aurelio Velázquez, Victor Alexandrov, and Mikhail Kharinov. Semantic analysis and recognition of raster-scanned color cartographic images. In Graphics Recognition Algorithms and Applications, International Workshop, Grec 2001, Kingston, Ontario, Canada, September 7-8, 2001, Selected Papers, pages 178-189, 2001.","title":"Semantic analysis and recognition of raster-scanned color cartographic images","authors":"Serguei Levachkine, Aurelio Velázquez, Victor Alexandrov, Mikhail Kharinov","year":"2001","venue":"Graphics Recognition Algorithms and Applications, International Workshop, Grec 2001","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Liu et al., 2016a] Tiange Liu, Qiguang Miao, Pengfei Xu, Jianfeng Song, and Yining Quan. Color topographical map segmentation algorithm based on linear element fea- tures. Multimedia Tools & Applications, 75(10):5417- 5438, 2016.","title":"Color topographical map segmentation algorithm based on linear element features","authors":"Liu","year":"2016","venue":"Multimedia Tools & Applications","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2016b] Tiange Liu, Qiguang Miao, Pengfei Xu, Yubing Tong, Jianfeng Song, Yun Yang, Yun Yang, and Xiaojie Zhai. A contour-line color layer separation algo- rithm based on fuzzy clustering and region growing. Com- puters & Geosciences, 88(C):41-53, 2016.","title":"A contour-line color layer separation algorithm based on fuzzy clustering and region growing","authors":"Liu","year":"2016","venue":"Computers & Geosciences","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Miao et al., 2013] Qiguang Miao, Pengfei Xu, Tiange Liu, Yyun Yang, Junying Zhang, and Weisheng Li. Linear fea- ture separation from topographic maps using energy den- sity and the shear transform. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 22(4):1546-1556, 2013.","title":"Linear feature separation from topographic maps using energy density and the shear transform","authors":"Miao","year":"2013","venue":"IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Miao et al., 2015] Qiguang Miao, Pengfei Xu, Tiange Liu, Jianfeng Song, and Xiaojiang Chen. A novel fast image segmentation algorithm for large topographic maps. Neu- rocomputing, 168(C):808-822, 2015.","title":"A novel fast image segmentation algorithm for large topographic maps","authors":"Miao","year":"2015","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Mignotte, 2008] Max Mignotte. Segmentation by fu- sion of histogram-based k-means clusters in different color spaces. IEEE Transactions on image processing, 17(5):780-787, 2008.","title":"Max Mignotte. Segmentation by fusion of histogram-based k-means clusters in different color spaces","authors":"Mignotte","year":"2008","venue":"IEEE Transactions on image processing","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Thorpe et al., 1996] Simon Thorpe, Denis Fize, and Cather- ine Marlot. Speed of processing in the human visual sys- tem. Nature, 381(6582):520-522, 1996.","title":"Speed of processing in the human visual system","authors":"Simon Thorpe, Denis Fize, Catherine Marlot","year":"1996","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Özdemir and Akarun, 2001] Dogan Özdemir and Lale Akarun. Fuzzy algorithms for combined quantization and dithering. IEEE Transactions on Image Processing, 10(6):923-931, 2001.","title":"Fuzzy algorithms for combined quantization and dithering","authors":"Dogan Özdemir, Lale Akarun","year":"2001","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Özdemir and Akarun, 2002] Dogan Özdemir and Lale Akarun. A fuzzy algorithm for color quantization of images. Pattern Recognition, 35(8):1785-1791, 2002.","title":"A fuzzy algorithm for color quantization of images","authors":"Dogan Özdemir, Lale Akarun","year":"2002","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}