Fetching the paper…
Reading the bibliography…
Indoor scene recognition is a multi-faceted and challenging problem due to the diverse intra-class variations and the confusing inter-class similarities.
A. Oliva and A. Torralba, “Modeling the shape of the scene: A holistic representation of the spatial envelope,” International Journal of Computer Vision , vol. 42, no. 3, pp. 145–175, 2001
2001
Earlier work this paper cites.
L. Fei-Fei and P. Perona, “A bayesian hierarchical model for learning natural scene categories,” in International Conference on Computer Vision and Pattern Recognition , vol. 2. IEEE, 2005, pp. 524–531
2005
Earlier work this paper cites.
S. Kumar and M. Hebert, “A hierarchical field framework for unified context-based classification,” in Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on , vol. 2. IEEE, 2005, pp. 1284–1291
2005
Earlier work this paper cites.
A. Opelt and A. Pinz, “Object localization with boosting and weak supervision for generic object recognition,” in SCIA . Springer, 2005, pp. 862–871
2005
Earlier work this paper cites.
S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” in International Conference on Computer Vision and Pattern Recognition , vol. 2. IEEE, 2006, pp. 2169–2178
2006
Earlier work this paper cites.
E. Nowak, F. Jurie, and B. Triggs, “Sampling strategies for bag-of-features image classification,” in European Conference on Computer Vision . Springer, 2006, pp. 490–503
2006
Earlier work this paper cites.
L.-J. Li and L. Fei-Fei, “What, where and who? classifying events by scene and object recognition,” in International Conference on Computer Vision . IEEE, 2007, pp. 1–8
2007
Earlier work this paper cites.
M. Marszatek and C. Schmid, “Accurate object localization with shape masks,” in International Conference on Computer Vision and Pattern Recognition . IEEE, 2007, pp. 1–8
2007
Earlier work this paper cites.
T. Tuytelaars and C. Schmid, “Vector quantizing feature space with a regular lattice,” in International Conference on Computer Vision . IEEE, 2007, pp. 1–8
2007
Earlier work this paper cites.
A. Bosch, A. Zisserman, and X. Muoz, “Scene classification using a hybrid generative/discriminative approach,” Transactions on Pattern Analysis and Machine Intelligence , vol. 30, no. 4, pp. 712–727, 2008
2008
Earlier work this paper cites.
F. Moosmann, E. Nowak, and F. Jurie, “Randomized clustering forests for image classification,” Transactions on Pattern Analysis and Machine Intelligence , vol. 30, no. 9, pp. 1632–1646, 2008
2008
Earlier work this paper cites.
A. Vedaldi and B. Fulkerson, “VLFeat: An open and portable library of computer vision algorithms,” http://www.vlfeat.org/
2008
Earlier work this paper cites.
A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth, “Describing objects by their attributes,” in International Conference on Computer Vision and Pattern Recognition . IEEE, 2009, pp. 1778–1785
2009
Earlier work this paper cites.
A. Quattoni and A. Torralba, “Recognizing indoor scenes,” in International Conference on Computer Vision and Pattern Recognition . IEEE, 2009
2009
Earlier work this paper cites.
H. Wang, M. M. Ullah, A. Klaser, I. Laptev, C. Schmid et al. , “Evaluation of local spatio-temporal features for action recognition,” in British Machine Vision Conference , 2009
2009
Earlier work this paper cites.
J. Wu and J. M. Rehg, “Beyond the euclidean distance: Creating effective visual codebooks using the histogram intersection kernel,” in International Conference on Computer Vision . IEEE, 2009, pp. 630–637
2009
Earlier work this paper cites.
Y.-L. Boureau, F. Bach, Y. LeCun, and J. Ponce, “Learning mid-level features for recognition,” in International Conference on Computer Vision and Pattern Recognition . IEEE, 2010, pp. 2559–2566
2010
Earlier work this paper cites.
S. Gao, I. W. Tsang, L.-T. Chia, and P. Zhao, “Local features are not lonely–laplacian sparse coding for image classification,” in International Conference on Computer Vision and Pattern Recognition . IEEE, 2010, pp. 3555–3561
2010
Earlier work this paper cites.
L.-J. Li, H. Su, L. Fei-Fei, and E. P. Xing, “Object bank: A high-level image representation for scene classification & semantic feature sparsification,” in Advances in Neural Information Processing Systems , 2010, pp. 1378–1386
2010
Earlier work this paper cites.
L. Torresani, M. Szummer, and A. Fitzgibbon, “Efficient object category recognition using classemes,” in European Conference on Computer Vision . Springer, 2010, pp. 776–789
2010
Cited alongside, same era.
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba, “Sun database: Large-scale scene recognition from abbey to zoo,” in International Conference on Computer Vision and Pattern Recognition . IEEE, 2010, pp. 3485–3492
2010
Cited alongside, same era.
2010
Cited alongside, same era.
J. Krapac, J. Verbeek, F. Jurie et al. , “Learning tree-structured descriptor quantizers for image categorization,” in British Machine Vision Conference , 2011
2011
Cited alongside, same era.
C. Doersch, A. Gupta, and A. A. Efros, “Mid-level visual element discovery as discriminative mode seeking,” in Advances in Neural Information Processing Systems , 2013, pp. 494–502
2013
Later among the works it cites.
S. Gupta, P. Arbelaez, and J. Malik, “Perceptual organization and recognition of indoor scenes from rgb-d images,” in International Conference on Computer Vision and Pattern Recognition . IEEE, 2013, pp. 564–571
2013
Later among the works it cites.
M. Juneja, A. Vedaldi, C. Jawahar, and A. Zisserman, “Blocks that shout: Distinctive parts for scene classification,” in International Conference on Computer Vision and Pattern Recognition . IEEE, 2013, pp. 923–930
2013
Later among the works it cites.
Q. Li, J. Wu, and Z. Tu, “Harvesting mid-level visual concepts from large-scale internet images,” in International Conference on Computer Vision and Pattern Recognition . IEEE, 2013, pp. 851–858
2013
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. Pandey and S. Lazebnik, “Scene recognition and weakly supervised object localization with deformable part-based models,” in International Conference on Computer Vision . IEEE, 2011, pp. 1307–1314
2011
Cited alongside, same era.
N. Silberman and R. Fergus, “Indoor scene segmentation using a structured light sensor,” in International Conference on Computer Vision Workshops , 2011
2011
Cited alongside, same era.
——, “Centrist: A visual descriptor for scene categorization,” Transactions on Pattern Analysis and Machine Intelligence , vol. 33, no. 8, pp. 1489–1501, 2011
2011
Cited alongside, same era.
J. Yuan, M. Yang, and Y. Wu, “Mining discriminative co-occurrence patterns for visual recognition,” in International Conference on Computer Vision and Pattern Recognition . IEEE, 2011, pp. 2777–2784
2011
Cited alongside, same era.
Y. Jiang, J. Yuan, and G. Yu, “Randomized spatial partition for scene recognition,” in European Conference on Computer Vision . Springer, 2012, pp. 730–743
2012
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems , 2012, pp. 1097–1105
2012
Cited alongside, same era.
R. Kwitt, N. Vasconcelos, and N. Rasiwasia, “Scene recognition on the semantic manifold,” in European Conference on Computer Vision . Springer, 2012, pp. 359–372
2012
Cited alongside, same era.
L.-J. Li, H. Su, Y. Lim, and L. Fei-Fei, “Objects as attributes for scene classification,” in Trends and Topics in Computer Vision . Springer, 2012, pp. 57–69
2012
Cited alongside, same era.
J. Sun and J. Ponce, “Learning discriminative part detectors for image classification and cosegmentation,” in International Conference on Computer Vision . IEEE, 2013, pp. 3400–3407
2013
Later among the works it cites.
D. Tao, L. Jin, Z. Yang, and X. Li, “Rank preserving sparse learning for kinect based scene classification.” IEEE transactions on cybernetics , vol. 43, no. 5, p. 1406, 2013
2013
Later among the works it cites.
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, “Return of the devil in the details: Delving deep into convolutional nets,” in British Machine Vision Conference , 2014
2014
Later among the works it cites.
Y. Gong, L. Wang, R. Guo, and S. Lazebnik, “Multi-scale orderless pooling of deep convolutional activation features,” in European Conference on Computer Vision . Springer International Publishing, 2014, pp. 392–407
2014
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” in European Conference on Computer Vision . Springer, 2014, pp. 346–361
2014
Later among the works it cites.
2014
Later among the works it cites.
D. Lin, C. Lu, R. Liao, and J. Jia, “Learning important spatial pooling regions for scene classification,” 2014
2014
Later among the works it cites.
R. Margolin, L. Zelnik-Manor, and A. Tal, “Otc: A novel local descriptor for scene classification,” in European Conference on Computer Vision . Springer, 2014, pp. 377–391
2014
Later among the works it cites.
M. Oquab, L. Bottou, I. Laptev, J. Sivic et al. , “Learning and transferring mid-level image representations using convolutional neural networks,” 2014
2014
Later among the works it cites.
2014
Later among the works it cites.
2014
Later among the works it cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “Imagenet large scale visual recognition challenge,” 2014
2014
Later among the works it cites.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European Conference on Computer Vision . Springer, 2014, pp. 818–833
2014
Later among the works it cites.
M. D. Zeiler, G. W. Taylor, and R. Fergus, “Adaptive deconvolutional networks for mid and high level feature learning,” in International Conference on Computer Vision . IEEE, 2011, pp. 2018–2025
2025
Closest in time.