A Deep Learning Pipeline for Semantic Facade Segmentation

Radwa Fathalla, George Vogiatzis

Research output: Chapter in Book/Report/Conference proceedingConference publication

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 that reflect both appearance and layout characteristics. On the other hand, they exhibit a degree of stability in the arrangement of structures across different instances. We integrate appearance and layout cues in a single framework. The most likely label based on appearance is obtained through applying the state-of-the-art deep convolution networks. This is further optimized through Restricted
Boltzmann Machines (RBM), applied on vertical and horizontal scanlines of facade models. Learning the probability distributions of the models via the RBMs is utilized in two settings. Firstly, we use them in learning from pre-seen facade samples, in the traditional training sense. Secondly, we learn from the test image at hand, in a way the allows the transfer of visual knowledge of the scene from correctly classified areas to others. Experimentally, we are on par with the reported performance results. However, we do not explicitly specify any hand-engineered features that are architectural scene dependent, nor do we include any dataset specific heuristics/thresholds.
Original languageEnglish
Title of host publicationProceedings of the British Machine Vision Conference 2016, BMVC 2017, September, 2017
Publication statusPublished - 30 Sep 2017
EventBritish Machine Vision Conference -
Duration: 10 Sep 2017 → …

Conference

ConferenceBritish Machine Vision Conference
Period10/09/17 → …

Bibliographical note

c 2017. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

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    Fathalla, R., & Vogiatzis, G. (2017). A Deep Learning Pipeline for Semantic Facade Segmentation. In Proceedings of the British Machine Vision Conference 2016, BMVC 2017, September, 2017