Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans

This paper proposes a runlength histogram signature as a perceptual descriptor of architectural plans in a retrieval scenario. The style of an architectural drawing is characterized by the perception of lines, shapes and texture. Such visual stimuli are the basis for defining semantic concepts as space properties, symmetry, density, etc. We propose runlength histograms extracted in vertical, horizontal and diagonal directions as a characterization of line and space properties in floorplans, so it can be roughly associated to a description of walls and room structure. A retrieval application illustrates the performance of the proposed approach, where given a plan as a query, similar ones are obtained from a database. A ground truth based on human observation has been constructed to validate the hypothesis. Additional retrieval results on sketched building’s facades are reported qualitatively in this paper. Its good description and its adaptability to two different sketch drawings despite its simplicity shows the interest of the proposed approach and opens a challenging research line in graphics recognition.

[1]  Axel Pinz,et al.  Computer Vision – ECCV 2006 , 2006, Lecture Notes in Computer Science.

[2]  Eric Saund,et al.  Finding Perceptually Closed Paths in Sketches and Drawings , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Eyke Hüllermeier,et al.  Preference Learning and Ranking by Pairwise Comparison , 2010, Preference Learning.

[4]  Sergei Vassilvitskii,et al.  Generalized distances between rankings , 2010, WWW '10.

[5]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[6]  Thomas M. Breuel,et al.  Document image zone classification - a simple high-performance approach , 2007, VISAPP.

[7]  Eyke Hllermeier,et al.  Preference Learning , 2010 .

[8]  Aurélie Lemaitre,et al.  A perceptive method for handwritten text segmentation , 2011, Electronic Imaging.

[9]  Thomas Deselaers,et al.  ClassCut for Unsupervised Class Segmentation , 2010, ECCV.

[10]  W. Chu Studying Aesthetics in Photographic Images Using a Computational Approach , 2013 .

[11]  Marcus Liwicki,et al.  a.SCatch: Semantic Structure for Architectural Floor Plan Retrieval , 2010, ICCBR.

[12]  Basilios Gatos,et al.  Page frame detection for double page document images , 2010, DAS '10.

[13]  Ernest Valveny,et al.  Unsupervised Wall Detector in Architectural Floor Plans , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[14]  Ernest Valveny,et al.  Large-scale document image retrieval and classification with runlength histograms and binary embeddings , 2013, Pattern Recognit..

[15]  Nuria Oliver,et al.  Towards Computational Models of the Visual Aesthetic Appeal of Consumer Videos , 2010, ECCV.