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   Vol.E103-A   No.12   pp.1599-1602
Publication Date: 2020/12/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.2020SML0004
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.