IEEE Transactions on Pattern Analysis and Machine Intelligence
Text/Graphics Separation Revisited
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Blurred Shape Model for binary and grey-level symbol recognition
Pattern Recognition Letters
International Journal of Computer Vision
A system to detect rooms in architectural floor plan images
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Improved Automatic Analysis of Architectural Floor Plans
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Wall Patch-Based Segmentation in Architectural Floorplans
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
A multi-agent system for the interpretation of architectural sketches
SBM'04 Proceedings of the First Eurographics conference on Sketch-Based Interfaces and Modeling
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Architectural floor plans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed in [1], showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. This paper presents an evolution of that previous work, after analyzing and testing several alternatives for each of the different steps of the method: Firstly, an automatic plan-size normalization process is done. Secondly we evaluate different features to obtain the description of every patch. Thirdly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These variations of the method have been tested for wall detection on two datasets of architectural floor plans with different notations. After studying in deep each of the steps in the process pipeline, we are able to find the best system configuration, which highly outperforms the results on wall segmentation obtained by the original paper.