Optimization of facade segmentation based on layout priors

Abstract

We propose an algorithm that provides a pixel-wise classification of building facades. Building facades provide a rich environment for testing semantic segmentation techniques. They come in a variety of styles affecting appearance and layout. On the other hand, they exhibit a degree of stability in the arrangement of structures across different instances. Furthermore, a single image is often composed of a repetitive architectural pattern. We integrate appearance, layout and repetition cues in a single energy function, that is optimized through the TRW-S algorithm to provide a classification of superpixels. The appearance energy is based on scores of a Random Forrest classifier. The feature space is composed of higher-level vectors encoding distance to structure clusters. Layout priors are obtained from locations and structural adjacencies in training data. In addition, priors result from translational symmetry cues acquired from the scene itself through clustering via the α -expansion graphcut algorithm. We are on par with state-of-the-art. We are able to fine tune classifications at the superpixel level, while most methods model all architectural features with bounding rectangles.

Publication DOI: https://doi.org/10.1007/978-3-319-64689-3_16
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 2017 Springer Publishing.
Event Title: 17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017
Event Type: Other
Event Dates: 2017-08-22 - 2017-08-24
Uncontrolled Keywords: Theoretical Computer Science,Computer Science(all)
ISBN: 978-3-3196-4688-6, 978-3-319-64689-3
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2017
Published Online Date: 2017-07-28
Accepted Date: 2017-07-28
Authors: Fathalla, Radwa
Vogiatzis, George (ORCID Profile 0000-0002-3226-0603)

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