M. Pandey and S. Lazebnik, Scene recognition and weakly supervised object localization with deformable part-based models, pp.1307-1314, 2011.

A. J. Bency, H. Kwon, H. Lee, S. Karthikeyan, and B. S. Manjunath, Weakly supervised localization using deep feature maps, Computer Vision-ECCV 2016-14th European Conference, pp.714-731, 2016.
DOI : 10.1007/978-3-319-46448-0_43
URL : http://arxiv.org/pdf/1603.00489

R. G. Cinbis, J. J. Verbeek, and C. Schmid, Weakly supervised object localization with multi-fold multiple instance learning, IEEE Trans. Pattern Anal. Mach. Intell, vol.39, issue.1, pp.189-203, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01123482

E. J. Crowley and A. Zisserman, Of gods and goats: Weakly supervised learning of figurative art, 2013.

O. Chum and A. Zisserman, An exemplar model for learning object classes, 2007.

C. Galleguillos, B. Babenko, A. Rabinovich, and S. J. Belongie, Weakly Supervised Object Localization with Stable Segmentations, pp.193-207, 2008.

P. Siva and T. Xiang, Weakly supervised object detector learning with model drift detection, pp.343-350, 2011.

T. Deselaers, B. Alexe, and V. Ferrari, Weakly Supervised Localization and Learning with Generic Knowledge, IJCV, vol.100, issue.3, pp.275-293, 2012.

H. Bilen, M. Pedersoli, and T. Tuytelaars, Weakly supervised object detection with convex clustering, IEEE Conference on Computer Vision and Pattern Recognition, pp.1081-1089, 2015.

H. Bilen and A. Vedaldi, Weakly supervised deep detection networks, 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp.2846-2854, 2016.

A. Diba, V. Sharma, A. M. Pazandeh, H. Pirsiavash, and L. V. Gool, Weakly supervised cascaded convolutional networks

M. H. Nguyen, L. Torresani, F. D. La-torre, and C. Rother, Learning discriminative localization from weakly labeled data, Pattern Recognition, vol.47, issue.3, pp.1523-1534, 2014.

M. Oquab, L. Bottou, I. Laptev, and J. Sivic, Is object localization for free?-weakly-supervised learning with convolutional neural networks, IEEE Conference on Computer Vision and Pattern Recognition, pp.685-694, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01015140

B. Zhou, A. Khosla, `. A. Lapedriza, A. Oliva, and A. Torralba, Learning deep features for discriminative localization, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.2921-2929, 2016.

J. Krapac and S. Segvi´csegvi´c, Weakly supervised object localization with large Fisher vectors, 2015.

V. Zadrija, J. Krapac, J. J. Verbeek, and S. Segvi´csegvi´c, Patch-level spatial layout for classification and weakly supervised localization, Pattern Recognition-37th German Conference, pp.492-503, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01186677

Y. Zhu, Y. Zhou, Q. Ye, Q. Qiu, and J. Jiao, Soft proposal networks for weakly supervised object localization, IEEE International Conference on Computer Vision, ICCV, pp.1859-1868, 2017.

K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, 2015.

J. Sánchez, F. Perronnin, T. Mensink, and J. J. Verbeek, Image Classification with the Fisher Vector: Theory and Practice, IJCV, vol.105, issue.3, pp.222-245, 2013.

Z. Qiu, T. Yao, and T. Mei, Deep quantization: Encoding convolutional activations with deep generative model

M. Cimpoi, S. Maji, I. Kokkinos, and A. Vedaldi, Deep filter banks for texture recognition, description, and segmentation, International Journal of Computer Vision, vol.118, issue.1, pp.65-94, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01263622

R. Jenatton, J. Mairal, G. Obozinski, and F. Bach, Proximal methods for hierarchical sparse coding, J. Mach. Learn. Res, vol.12, pp.2297-2334, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00516723

D. Novotn´ynovotn´y, D. Larlus, F. Perronnin, and A. Vedaldi, Understanding the fisher vector: a multimodal part model

D. Ahmetovic, R. Manduchi, J. M. Coughlan, and S. Mascetti, Zebra crossing spotter: Automatic population of spatial databases for increased safety of blind travelers, Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility, ASSETS 2015, pp.251-258, 2015.

D. Koester, B. Lunt, and R. Stiefelhagen, Zebra crossing detection from aerial imagery across countries, Computers Helping People with Special Needs-15th International Conference, vol.9759, pp.27-34, 2016.
DOI : 10.1007/978-3-319-41267-2_5

, ICMLA 2011 StreetView Recognition Challenge, Online, pp.2016-2027, 2016.

. Promet-i-prostor, E-roads web platform, Online; accessed, pp.2016-2025

J. Sánchez, F. Perronnin, T. Mensink, and J. Verbeek, Image Classification with the Fisher Vector: Theory and Practice, International Journal of Computer Vision, vol.105, issue.3, pp.222-245, 2013.

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.68, issue.1, pp.49-67, 2006.
DOI : 10.1111/j.1467-9868.2005.00532.x

F. Perronnin, J. Sánchez, and T. Mensink, Improving the Fisher kernel for large-scale image classification, pp.143-156, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00548630

T. Jaakkola and D. Haussler, Exploiting generative models in discriminative classifiers, pp.487-493, 1998.

F. Perronnin and C. R. Dance, Fisher kernels on visual vocabularies for image categorization, 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp.18-23, 2007.
DOI : 10.1109/cvpr.2007.383266

R. Arandjelovi´carandjelovi´c and A. Zisserman, All about VLAD, IEEE Conference on Computer Vision and Pattern Recognition, 2013.

C. Bishop, Pattern recognition and machine learning, 2006.

Z. Zhang, Y. Xu, J. Yang, X. Li, and D. Zhang, A survey of sparse representation: Algorithms and applications, IEEE Access, vol.3, pp.490-530, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01311245

J. Krapac and S. Segvi´csegvi´c, Fast Approximate GMM Soft-Assign for Fine-Grained Image Classification with Large Fisher Vectors, 2015.
DOI : 10.1007/978-3-319-24947-6_39
URL : https://doi.org/10.1007/978-3-319-24947-6_39

P. A. Viola and M. J. Jones, Robust real-time face detection, International Journal of Computer Vision, vol.57, issue.2, pp.137-154, 2004.
DOI : 10.1109/iccv.2001.937709

O. Wiki, Map Features-OpenStreetMap Wiki, pp.2016-2027, 2016.

. Geofabrik, OpenStreetMap Data Extracts, Online; accessed, pp.2016-2027

, OpenStreetMap, Online, OpenStreetMap Foundation, pp.2016-2027, 2016.

O. Wiki, A. Overpass, and O. Wiki, , pp.2016-2025

O. Wiki, Video mapping-OpenStreetMap Wiki, pp.2016-2027, 2016.

. Mapillary, Mapillary mobile application, online; accessed, pp.2016-2027

T. Gmbh, OpenStreetView mobile application, Online; accessed, pp.2016-2027

M. Everingham, L. Gool, C. K. Williams, J. Winn, and A. Zisserman, The Pascal visual object classes (VOC) challenge, Int. J. Comput. Vision, vol.88, issue.2, pp.303-338, 2010.
DOI : 10.1007/s11263-009-0275-4

K. Chatfield, V. S. Lempitsky, A. Vedaldi, and A. Zisserman, The devil is in the details: an evaluation of recent feature encoding methods, British Machine Vision Conference, pp.1-12, 2011.

R. M. French, Catastrophic forgetting in connectionist networks: Causes, consequences and solutions, Trends in Cognitive Sciences, vol.3, issue.4, pp.128-135, 1999.

M. Douze and H. Jégou, The Yael library, Proceedings of the ACM International Conference on Multimedia, 2014.
DOI : 10.1145/2647868.2654892
URL : https://hal.archives-ouvertes.fr/hal-01020695

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, Online learning for matrix factorization and sparse coding, J. Mach. Learn. Res, vol.11, pp.19-60, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00408716

, Convention on road signs and signals, 1968.

K. Brki´cbrki´c, A. Pinz, and S. Segvi´csegvi´c, Histogram-based description of local space-time appearance, Z. Kalafati´cKalafati´c, pp.206-217, 2011.

N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, 2005.
DOI : 10.1109/cvpr.2005.177
URL : https://hal.archives-ouvertes.fr/inria-00548512