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Acceleration of Automatic Building Extraction via Color-Clustering Analysis
Masakazu IWAI Takuya FUTAGAMI Noboru HAYASAKA Takao ONOYE
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2020/12/01
Online ISSN: 1745-1337
Type of Manuscript: Special Section LETTER (Special Section on Smart Multimedia & Communication Systems)
Category: Computer Graphics
scenery image, building extraction, GrabCut, acceleration,
Full Text: FreePDF(3.3MB)
In this paper, we improve upon the automatic building extraction method, which uses a variational inference Gaussian mixture model for performing color clustering, by accelerating its computational speed. The improved method decreases the computational time using an image with reduced resolution upon applying color clustering. According to our experiment, in which we used 106 scenery images, the improved method could extract buildings at a rate 86.54% faster than that of the conventional methods. Furthermore, the improved method significantly increased the extraction accuracy by 1.8% or more by preventing over-clustering using the reduced image, which also had a reduced number of the colors.