Deep learning the city: Quantifying urban perception at a global scale

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9905 LNCS, Issue , 2016, Pages 196-212

  Dubey, Abhimanyu ^a   Naik, Nikhil ^c   Parikh, Devi ^b   Raskar, Ramesh ^c   Hidalgo, César A ^c  

^a INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

^b VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^c MIT MEDIA LAB   (United States)

Author keywords

Attributes; Crowdsourcing; Perception; Street view

Indexed keywords

COMPUTER VISION; CROWDSOURCING; SENSORY PERCEPTION;

ATTRIBUTES; GLOBAL SCALE; HUMAN JUDGMENTS; IMAGE COMPARISON; NEURAL ARCHITECTURES; PAIR-WISE COMPARISON; PERCEPTUAL ATTRIBUTES; URBAN ENVIRONMENTS;

DEEP LEARNING;

EID: 84990055093     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46448-0_12     Document Type: Conference Paper

References (58)

1. Wilson, J.Q., Kelling, G.L.: Broken windows. Atlantic Monthly 249(3), 29–38 (1982)
2. Keizer, K., Lindenberg, S., Steg, L.: The spreading of disorder. Science 322(5908), 1681–1685 (2008)
3. Milam, A., Furr-Holden, C., Leaf, P.: Perceived school and neighborhood safety, neighborhood violence and academic achievement in urban school children. Urban Rev. 42(5), 458–467 (2010)
4. Cohen, D.A., Mason, K., Bedimo, A., Scribner, R., Basolo, V., Farley, T.A.: Neighborhood physical conditions and health. Am. J. Public Health 93(3), 467–471 (2003)
5. Piro, F.N., Noess, Ø., Claussen, B.: Physical activity among elderly people in a city population: the influence of neighbourhood level violence and self perceived safety. J. Epidemiol. Commun. Health 60(7), 626–632 (2006)
6. Sampson, R.J.: Great American City: Chicago and the enduring neighborhood effect. University of Chicago Press, Chicago (2012)
7. Miller, D.K.: Using google street view to audit the built environment: inter-rater reliability results. Ann. Behav. Med. 45(1), 108–112 (2013)
8. Hwang, J., Sampson, R.J.: Divergent pathways of gentrification racial inequality and the social order of renewal in chicago neighborhoods. Am. Sociol. Rev. 79(4), 726–751 (2014)
9. Salesses, P., Schechtner, K., Hidalgo, C.A.: The collaborative image of the city: mapping the inequality of urban perception. PloS One 8(7), e68–400 (2013)
10. Quercia, D., O’Hare, N.K., Cramer, H.: Aesthetic capital: what makes London look beautiful, quiet, and happy? In: ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 945–955 (2014)
11. Naik, N., Philipoom, J., Raskar, R., Hidalgo, C.: Streetscore-Predicting the perceived safety of one million streetscapes. In: IEEE CVPR Workshops, pp. 793–799 (2014)
12. Ordonez, V., Berg, T.L.: Learning high-level judgments of urban perception. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 494–510. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10599-4_32
13. Porzi, L., Rota Bulò, S., Lepri, B., Ricci, E.: Predicting and understanding urban perception with convolutional neural networks. In: ACM Conference on Multimedia, pp. 139–148 (2015)
14. Naik, N., Raskar, R., Hidalgo, C.A.: Cities are physical too: using computer vision to measure the quality and impact of urban appearance. Am. Econ. Rev. 106(5), 128–132 (2016)
15. Been, V., Ellen, I.G., Gedal, M., Glaeser, E., McCabe, B.J.: Preserving history or restricting development? the heterogeneous effects of historic districts on local housing markets in new york city. J. Urban Econ. 92, 16–30 (2015)
16. Naik, N., Kominers, S.D., Raskar, R., Glaeser, E.L., Hidalgo, C.A.: Do people shape cities, or do cities shape people? the co-evolution of physical, social, and economic change in five major U.S. cities. Working Paper 21620, National Bureau of Economic Research (2015)
17. Harvey, C., Aultman-Hall, L., Hurley, S.E., Troy, A.: Effects of skeletal streetscape design on perceived safety. Landscape Urban Plann. 142, 18–28 (2015)
18. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
19. Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Advances in Neural Information Processing Systems, pp. 487–495 (2014)
20. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
21. Joshi, D., Datta, R., Fedorovskaya, E., Luong, Q.T., Wang, J.Z., Li, J., Luo, J.: Aesthetics and emotions in images. IEEE Sig. Process. Mag. 28(5), 94–115 (2011)
22. Isola, P., Xiao, J., Torralba, A., Oliva, A.: What makes an image memorable? In: IEEE CVPR, pp. 145–152 (2011)
23. Dhar, S., Ordonez, V.: Berg, T.L.: High level describable attributes for predicting aesthetics and interestingness. In: IEEE CVPR, pp. 1657–1664 (2011)
24. Deza, A., Parikh, D.: Understanding image virality. In: IEEE CVPR, pp. 1818–1826 (2015)
25. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: a deep convolutional activation feature for generic visual recognition (2013). arXiv preprint arXiv:1310.1531
26. Doersch, C., Singh, S., Gupta, A., Sivic, J., Efros, A.A.: What makes paris look like paris? ACM Trans. Graph. 31(4), 101 (2012)
27. Lee, S., Maisonneuve, N., Crandall, D., Efros, A., Sivic, J.: Linking past to present: discovering style in two centuries of architecture. In: IEEE International Conference on Computational Photography (2015)
28. Arietta, S.M., Efros, A.A., Ramamoorthi, R., Agrawala, M.: City forensics: using visual elements to predict non-visual city attributes. IEEE Trans. Visual. Comput. Graph. 20(12), 2624–2633 (2014)
29. Glaeser, E.L., Kominers, S.D., Luca, M., Naik, N.: Big data and big cities: the promises and limitations of improved measures of urban life. Working Paper 21778, National Bureau of Economic Research (2015)
30. Zhou, B., Liu, L., Oliva, A., Torralba, A.: Recognizing city identity via attribute analysis of geo-tagged images. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 519–534. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10578-9_34
31. Khosla, A., An, B., Lim, J.J., Torralba, A.: Looking beyond the visible scene. In: IEEE CVPR, pp. 3710–3717 (2014)
32. Kuipers, M.A., van Poppel, M.N., van den Brink, W., Wingen, M., Kunst, A.E.: The association between neighborhood disorder, social cohesion and hazardous alcohol use: a national multilevel study. Drug Alcohol Depend. 126(1), 27–34 (2012)
33. Dulin-Keita, A., Thind, H.K., Affuso, O., Baskin, M.L.: The associations of perceived neighborhood disorder and physical activity with obesity among african american adolescents. BMC Pub. Health 13(1), 440 (2013)
34. Kelling, G.L., Coles, C.M.: Fixing BrokenWindows: Restoring Order and Reducing Crime in Our Communities. Simon and Schuster, New York (1997)
35. Sampson, R.J., Raudenbush, S.W.: Disorder in urban neighborhoods: Does it lead to crime. National Institute of Justice (2001)
36. Harcourt, B.E.: Reflecting on the subject: a critique of the social influence conception of deterrence, the broken windows theory, and order-maintenance policing New York style. Mich. Law Rev. 97(2), 291–389 (1998)
37. Parikh, D., Grauman, K.: Relative attributes. In: IEEE ICCV, pp. 503–510 (2011)
38. Parkash, A., Parikh, D.: Attributes for classifier feedback. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 354–368. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33712-3_26
39. Kovashka, A., Parikh, D., Grauman, K.: Whittlesearch: Image search with relative attribute feedback. In: IEEE CVPR, pp. 2973–2980 (2012)
40. Kiapour, M.H., Yamaguchi, K., Berg, A.C., Berg, T.L.: Hipster wars: discovering elements of fashion styles. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 472–488. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10590-1_31
41. Zhu, J.Y., Agarwala, A., Efros, A.A., Shechtman, E., Wang, J.: Mirror mirror: crowdsourcing better portraits. ACM Trans. Graph. 33(6), 234 (2014)
42. Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., Wu, Y.: Learning fine-grained image similarity with deep ranking. In: IEEE CVPR, pp. 1386–1393 (2014)
43. Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: IEEE CVPR, pp. 4353–4361 (2015)
44. Persson, P.O., Strang, G.: A simple mesh generator in MATLAB. SIAM Rev. 46(2), 329–345 (2004)
45. Stewart, N., Brown, G.D., Chater, N.: Absolute identification by relative judgment. Psychol. Rev. 112(4), 881 (2005)
46. Bijmolt, T.H., Wedel, M.: The effects of alternative methods of collecting similarity data for multidimensional scaling. Int. J. Res. Mark. 12(4), 363–371 (1995)
47. Jou, B., Bhattacharya, S., Chang, S.F.: Predicting viewer perceived emotions in animated GIFs. In: ACM International Conference on Multimedia, pp. 213–216 (2014)
48. Sartori, A., Yanulevskaya, V., Salah, A.A., Uijlings, J., Bruni, E., Sebe, N.: Affective analysis of professional and amateur abstract paintings using statistical analysis and art theory. ACM Trans. Interact. Intell. Syst. 5(2), 8 (2015)
49. Herbrich, R., Minka, T., Graepel, T.: TrueSkill: a bayesian skill rating system. In: Advances in Neural Information Processing Systems, pp. 569–576 (2006)
50. Joachims, T.: Optimizing search engines using clickthrough data. In: ACM International Conference on Knowledge Discovery and Data Mining, pp. 133–142 (2002)
51. Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: IEEE CVPR, vol. 1, pp. 539–546 (2005)
52. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Largescale video classification with convolutional neural networks. In: IEEE CVPR, pp. 1725–1732 (2014)
53. Chapelle, O., Keerthi, S.S.: Efficient algorithms for ranking with SVMs. Inf. Retrieval 13(3), 201–215 (2010)
54. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: ACM International Conference on Multimedia, pp. 675–678 (2014)
55. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
56. Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)
57. Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. Int. J. Comput. Vis. 43(1), 7–27 (2001)
58. Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: IEEE CVPR, pp. 3485–3492 (2010)