Navigation using special buildings as signposts

Navigation has been greatly improved by positioning systems, but visualization still relies on maps. Yet because they only represent an abstract street network, maps are sometimes difficult to read. Conversely, Tourist Maps, which are enriched with landmark drawings, have been shown to be much more intuitive to understand. However, outside the very centres of cities, major landmarks are too sparse to be helpful. In this work, we present a method to automatically augment maps with most locally prominent such buildings, at multiple scale. Further, we generate a characterization which helps emphasize the special attributes of these buildings. Descriptive features are extracted from facades, analyzed and re-ranked to match human perception. To do so, we collected a total number of over 5900 human annotations to characterize 117 facades across 3 different cities. Finally, the characterizations are also used to produce natural language descriptions of the facades.

[1]  Yejin Choi,et al.  Baby talk: Understanding and generating simple image descriptions , 2011, CVPR 2011.

[2]  M. Blades,et al.  HOW DO PEOPLE USE MAPS TO NAVIGATE THROUGH THE WORLD , 1987 .

[3]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[4]  Vic Barnett,et al.  Outliers in Statistical Data , 1980 .

[5]  Iasonas Kokkinos,et al.  Shape grammar parsing via Reinforcement Learning , 2011, CVPR 2011.

[6]  Luc Van Gool,et al.  Visual interestingness in image sequences , 2013, MM '13.

[7]  D. Sculley,et al.  Combined regression and ranking , 2010, KDD.

[8]  Hayko Riemenschneider,et al.  Irregular lattices for complex shape grammar facade parsing , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Adam Finkelstein,et al.  Directing gaze in 3D models with stylized focus , 2006, EGSR '06.

[10]  Karl Stratos,et al.  Understanding and predicting importance in images , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[12]  Luc Van Gool,et al.  A Three-Layered Approach to Facade Parsing , 2012, ECCV.

[13]  Pedram Sadeghian,et al.  The New Generation of Automatic Landmark Detection Systems: Challenges and Guidelines , 2008, Spatial Cogn. Comput..

[14]  Alexei A. Efros,et al.  What makes Paris look like Paris? , 2015, Commun. ACM.

[15]  Pieter Peers,et al.  Content-adaptive image downscaling , 2013, ACM Trans. Graph..

[16]  Stephan Winter,et al.  Selection of Salient Features for Route Directions , 2004, Spatial Cogn. Comput..

[17]  Maneesh Agrawala,et al.  Automatic generation of tourist maps , 2008, ACM Trans. Graph..

[18]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  M. Raubal Formalizing Conceptual Spaces , 2004 .

[20]  Stephan Winter,et al.  Enriching Wayfinding Instructions with Local Landmarks , 2002, GIScience.

[21]  Michel Denis,et al.  Referring to Landmark or Street Information in Route Directions: What Difference Does It Make? , 2003, COSIT.

[22]  Jianxiong Xiao,et al.  Image-based façade modeling , 2008, ACM Trans. Graph..

[23]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[24]  Pascal Hitzler,et al.  Formalizing Ontology Alignment and its Operations with Category Theory , 2006, FOIS.

[25]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[26]  Devi Parikh,et al.  Attribute Dominance: What Pops Out? , 2013, 2013 IEEE International Conference on Computer Vision.

[27]  Luc Van Gool,et al.  Procedural modeling of buildings , 2006, ACM Trans. Graph..

[28]  M. Raubal,et al.  Focalizing measures of salience for wayfinding , 2005 .

[29]  Cordelia Schmid,et al.  Learning Color Names from Real-World Images , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.