2017

CNN features are also great at unsupervised classification

Guérin, Joris, Gibaru, Olivier, Thiery, Stéphane et al.

Understand

This paper aims at providing insight on the transferability of deep CNN features to unsupervised problems.

We study the impact of different pretrained CNN feature extractors on the problem of image set clustering for object classification as well as fine-grained classification.
We propose a rather straightforward pipeline combining deep-feature extraction using a CNN pretrained on ImageNet and a classic clustering algorithm to classify sets of images.
This approach is compared to state-of-the-art algorithms in image-clustering and provides better results.

Built on

F. Murtagh, “A survey of recent advances in hierarchical clustering algorithms,” The Computer Journal , vol. 26, no. 4, pp. 354–359, 1983
1983
Earlier work this paper cites.
F. S. Samaria and A. C. Harter, “Parameterisation of a stochastic model for human face identification,” in Applications of Computer Vision, 1994., Proceedings of the Second IEEE Workshop on . IEEE, 1994, pp. 138–142
1994
Earlier work this paper cites.
M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic et al. , “Query by image and video content: The qbic system,” computer , vol. 28, no. 9, pp. 23–32, 1995
1995
Earlier work this paper cites.
M. Ester, H.-P. Kriegel, J. Sander, X. Xu et al. , “A density-based algorithm for discovering clusters in large spatial databases with noise.” in Kdd , vol. 96, no. 34, 1996, pp. 226–231
1996
Earlier work this paper cites.
T. Zhang, R. Ramakrishnan, and M. Livny, “Birch: an efficient data clustering method for very large databases,” in ACM Sigmod Record , vol. 25, no. 2. ACM, 1996, pp. 103–114
1996
Earlier work this paper cites.
S. Nayar, S. Nene, and H. Murase, “Columbia object image library (coil 100),” Department of Comp. Science, Columbia University, Tech. Rep. CUCS-006-96 , 1996
1996
Earlier work this paper cites.
N. Tishby, F. C. Pereira, and W. Bialek, “The information bottleneck method,” arXiv preprint physics/0004057 , 2000
2000
Earlier work this paper cites.
J. C. Bezdek and R. J. Hathaway, “Some notes on alternating optimization,” in AFSS International Conference on Fuzzy Systems . Springer, 2002, pp. 288–300
2002
Earlier work this paper cites.
D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Transactions on pattern analysis and machine intelligence , vol. 24, no. 5, pp. 603–619, 2002
2002
Earlier work this paper cites.
S. Theodoridis and K. Koutroumbas, “Pattern recognition,” 2003
2003
Earlier work this paper cites.
Y. Seldin, S. Starik, and M. Werman, “Unsupervised clustering of images using their joint segmentation,” in Proceedings of the 3rd International Workshop on Statistical and Computational Theories of Vision (SCTV 2003) , 2003
2003
Earlier work this paper cites.
J. Goldberger, S. Gordon, and H. Greenspan, “Unsupervised image-set clustering using an information theoretic framework,” IEEE transactions on image processing , vol. 15, no. 2, pp. 449–458, 2006
2006
Earlier work this paper cites.
D. Nister and H. Stewenius, “Scalable recognition with a vocabulary tree,” in Computer vision and pattern recognition, 2006 IEEE computer society conference on , vol. 2. Ieee, 2006, pp. 2161–2168
2006
Earlier work this paper cites.
M. Everingham, A. Zisserman, C. K. Williams, L. Van Gool, M. Allan, C. M. Bishop, O. Chapelle, N. Dalal, T. Deselaers, G. Dorkó et al. , “The pascal visual object classes challenge 2007 (voc2007) results,” 2007
2007
Earlier work this paper cites.
D. Arthur and S. Vassilvitskii, “k-means++: The advantages of careful seeding,” in Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms . Society for Industrial and Applied Mathematics, 2007, pp. 1027–1035
2007
Earlier work this paper cites.

Similar

D. Dueck and B. J. Frey, “Non-metric affinity propagation for unsupervised image categorization,” in Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on . IEEE, 2007, pp. 1–8
2007
Cited alongside, same era.
Y. J. Lee and K. Grauman, “Shape discovery from unlabeled image collections,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on . IEEE, 2009, pp. 2254–2261
2009
Cited alongside, same era.
A. Quattoni and A. Torralba, “Recognizing indoor scenes,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on . IEEE, 2009, pp. 413–420
2009
Cited alongside, same era.
D. Sculley, “Web-scale k-means clustering,” in Proceedings of the 19th international conference on World wide web . ACM, 2010, pp. 1177–1178
2010
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.
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “Overfeat: Integrated recognition, localization and detection using convolutional networks,” arXiv preprint arXiv:1312.6229 , 2013
Original
2013
Cited alongside, same era.
L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. VanderPlas, A. Joly, B. Holt, and G. Varoquaux, “API design for machine learning software: experiences from the scikit-learn project,” in ECML PKDD Workshop: Languages for Data Mining and Machine Learning , 2013, pp. 108–122
2013
Cited alongside, same era.
A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “Cnn features off-the-shelf: an astounding baseline for recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2014, pp. 806–813
2014
Cited alongside, same era.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Advances in neural information processing systems , 2014, pp. 3320–3328
2014
Cited alongside, same era.
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, “Decaf: A deep convolutional activation feature for generic visual recognition.” in Icml , vol. 32, 2014, pp. 647–655
2014
Cited alongside, same era.
T. Fukui and T. Wada, “Commonality preserving image-set clustering based on diverse density,” in International Symposium on Visual Computing . Springer, 2014, pp. 258–269
2014
Cited alongside, same era.
G. Kim, L. Sigal, and E. P. Xing, “Joint summarization of large-scale collections of web images and videos for storyline reconstruction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 4225–4232
2014
Cited alongside, same era.

Then

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision , vol. 115, no. 3, pp. 211–252, 2015
2015
Later among the works it cites.
Y. Gong, M. Pawlowski, F. Yang, L. Brandy, L. Bourdev, and R. Fergus, “Web scale photo hash clustering on a single machine,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 19–27
2015
Later among the works it cites.
Y. Avrithis, Y. Kalantidis, E. Anagnostopoulos, and I. Z. Emiris, “Web-scale image clustering revisited,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 1502–1510
2015
Later among the works it cites.
F. Chollet, “Keras,” 2015
2015
Later among the works it cites.
R. B. Girshick, “Fast R-CNN,” CoRR , vol. abs/1504.08083, 2015. [Online]. Available: http://arxiv.org/abs/1504.08083
Original
2015
Later among the works it cites.
S. Uchida, S. Ide, B. K. Iwana, and A. Zhu, “A further step to perfect accuracy by training cnn with larger data,” in Frontiers in Handwriting Recognition (ICFHR), 2016 15th International Conference on . IEEE, 2016, pp. 405–410
2016
Later among the works it cites.
H. Liu, M. Shao, S. Li, and Y. Fu, “Infinite ensemble for image clustering,” in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2016
2016
Later among the works it cites.
J. Yang, D. Parikh, and D. Batra, “Joint unsupervised learning of deep representations and image clusters,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 5147–5156
2016
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
Later among the works it cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2818–2826
2016
Later among the works it cites.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” arXiv preprint arXiv:1610.02357 , 2016
Original
2016
Later among the works it cites.
N. Dhanachandra and Y. J. Chanu, “A survey on image segmentation methods using clustering techniques,” European Journal of Engineering Research and Science , vol. 2, no. 1, 2017
2017
Closest in time.
X. Wang, L. Lu, H.-c. Shin, L. Kim, M. Bagheri, I. Nogues, J. Yao, and R. M. Summers, “Unsupervised joint mining of deep features and image labels for large-scale radiology image categorization and scene recognition,” arXiv preprint arXiv:1701.06599 , 2017
Original
2017
Closest in time.
J. Guérin, O. Gibaru, S. Thiery, and E. Nyiri, “Clustering for different scales of measurement-the gap-ratio weighted k-means algorithm,” arXiv preprint arXiv:1703.07625 , 2017
Original
2017
Closest in time.

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