Construction site image retrieval based on material cluster recognition |
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| Authors: | Ioannis K Brilakis Lucio Soibelman Yoshihisa Shinagawa |
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| Affiliation: | aDepartment of Civil and Environmental Engineering, University of Michigan, 2340 G.G. Brown, 2350 Hayward St., Ann Arbor, MI 48109-2125, USA;bCarnegie Mellon University, Porter Hall 118N, Pittsburgh, PA 15213-3890, USA;cUniversity of Illinois, 2021 Beckman, 405 N. Mathews Ave, Urbana, IL 61801, USA |
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| Abstract: | The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of image processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images. |
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| Keywords: | Images Construction Image retrieval Information retrieval Materials Clustering |
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