Adaptive spatial pooling for image classification

Pattern Recognition

Volumn 55, Issue , 2016, Pages 58-67

  Liu, Yinglu ^a   Zhang, Yan Ming ^a   Zhang, Xu Yao ^a   Liu, Cheng Lin ^a,b  

^a INSTITUTE OF AUTOMATION   (China)

^b Chinese Academy of Sciences   (China)

Author keywords

Distribution matrix; Image classification; Spatial layout; Weighted pooling

Indexed keywords

LINEAR TRANSFORMATIONS; MATHEMATICAL TRANSFORMATIONS; SPATIAL DISTRIBUTION;

BENCHMARK DATASETS; CATEGORY SPECIFICS; CLASSIFIER TRAINING; DISTRIBUTION MATRICES; FEATURE REPRESENTATION; RECTANGULAR REGIONS; SPATIAL LAYOUT; WEIGHTED POOLING;

IMAGE CLASSIFICATION;

EID: 84977742944     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2016.01.030     Document Type: Article

References (52)

1. G. Csurka, C. Dance, L. Fan, J. Willamowski, C. Bray, Visual categorization with bags of keypoints, In: European Conference on Computer Vision, 2004, pp. 1-22.
2. S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: spatial pyramid matching for recognizing natural scene categories, In: Computer Vision and Pattern Recognition, vol. 2, IEEE, New York, 2006, pp. 2169-2178.
3. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner Gradient-based learning applied to document recognition Proc. IEEE 86 11 1998 2278 2324
4. A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, In: Advances in Neural Information Processing Systems, 2012, pp. 1097-1105.
5. C. Cortes, and V. Vapnik Support-vector networks Mach. Learn. 20 1995 273 297
6. C.-C. Chang, and C.-J. Lin LIBSVM: a library for support vector machines ACM Trans. Intell. Syst. Technol. 2 3 2011 27
7. A. Jain, J. Mao, and K. Mohluddin Artificial neural networks: a tutorial IEEE Comput. 29 3 1996 31 44
8. R.E. Schapire, The boosting approach to machine learning: an overview, In: Workshop on Nonlinear Estimation and Classification, 2002.
9. C. Zhang, J. Liu, Q. Tian, Y. Han, H. Lu, and S. Ma A boosting, sparsity-constrained bilinear model for object recognition IEEE MultiMed. 19 2 2012 58 68
10. Y.-L. Boureau, J. Ponce, Y. LeCun, A theoretical analysis of feature pooling in visual recognition, In: International Conference on Machine Learning, IEEE, Haifa, 2010, pp. 111-118.
11. Y. MarcAurelio Ranzato, L. Boureau, Y. LeCun, Sparse feature learning for deep belief networks, In: Advances in Neural Information Processing Systems, vol. 20, 2007, pp. 1185-1192.
12. J. Yang, K. Yu, Y. Gong, T. Huang, Linear spatial pyramid matching using sparse coding for image classification, In: Computer Vision and Pattern Recognition, IEEE, Miami, 2009, pp. 1794-1801.
13. J. Feng, B. Ni, Q. Tian, S. Yan, Geometric lp-norm feature pooling for image classification, In: Computer Vision and Pattern Recognition, IEEE, Colorado Springs, 2011, pp. 2609-2704.
14. Y. Jiang, J. Yuan, G. Yu, Randomized spatial partition for scene recognition, In: European Conference on Computer Vision, Springer, Firenze, 2012, pp. 730-743.
15. Y. Jia, C. Huang, T. Darrell, Beyond spatial pyramids: receptive field learning for pooled image features, In: Computer Vision and Pattern Recognition, IEEE, Providence, 2012, pp. 3370-3377.
16. O. Russakovsky, Y. Lin, K. Yu, F.-F. Li, Object-centric spatial pooling for image classification, In: European Conference on Computer Vision, Springer, Firenze, 2012, pp. 1-15.
17. J. Sánchez, F. Perronnin, and T. Campos Modeling the spatial layout of images beyond spatial pyramids Pattern Recognit. Lett. 33 2012 2216 2223
18. T. Harada, Y. Ushiku, Y. Yamashita, Y. Kuniyoshi, Discriminative spatial pyramid, In: Computer Vision and Pattern Recognition, IEEE, Colorado Springs, 2011, pp. 1617-1624.
19. Y. Huang, Z. Wu, L. Wang, and C. Song Multiple spatial pooling for visual object recognition Neurocomputing 129 2014 225 231
20. M. Malinowski, M. Fritz, Learning smooth pooling regions for visual recognition, In: British Machine Vision Conference, 2013.
21. D. Ciresan, U. Meier, J. Schmidhuber, Multi-column deep neural networks for image classification, In: Computer Vision and Pattern Recognition, IEEE, Providence, 2012, pp. 3642-3649.
22. D.C. Ciresan, U. Meier, J. Masci, L.M. Gambardella, J. Schmidhuber, Flexible, high performance convolutional neural networks for image classification, In: International Joint Conference on Artificial Intelligence, ACM, Barcelona, 2011, pp. 1237-1242.
23. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, arXiv:1409.4842, 2014.
24. < http://pascallin.ecs.soton.ac.uk/challenges/VOC/ >.
25. < http://trdcvid.nist.gov/ >.
26. C. Harris, M. Stephens, A combined corner and edge detector, In: Alvey Vision Conference, 1988, pp. 147-151.
27. T. Tuytelaars, and L.V. Gool Matching widely separated views based on affine invariant regions Int. J. Comput. Vis. 59 2004 61 85
28. D.G. Lowe Distinctive image features from scale-invariant keypoints Int. J. Comput. Vis. 60 2 2004 91 110
29. J. Matas, O. Chum, M. Urban, T. Pajdla, Robust wide baseline stereo from maximally stable extremal regions, In: British Machine Vision Conference, BMVA Press, University of Cardiff, 2002, pp. 36.1-36.10.
30. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, In: Computer Vision and Pattern Recognition, IEEE, San Diego, 2005, pp. 886-893.
31. H. Bay, A. Ess, T. Tuytelaars, and L.V. Gool Surf: speeded up robust features Comput. Vis. Image Underst. 110 3 2008 346 359
32. T. Ojala, M. Pietikainen, D. Harwood, Performance evaluation of texture measures with classification based on Kullback discrimination of distributions, In: International Conference on Pattern Recognition, vol. 1, 1994, pp. 582-585.
33. S.P. Lloyd Least squares quantization in PCM IEEE Trans. Inf. Theory 28 2 1982 129 137
34. J. Mairal, F. Bach, Ponce, J., G. Sapiro, A. Zisserman, Supervised dictionary learning, In: Advances in Neural Information Processing Systems, 2008.
35. I. Ramirez, P. Sprechmann, G. Sapiro, Classification and clustering via dictionary learning with structured incoherence and shared features, In: Computer Vision and Pattern Recognition, IEEE, San Francisco, 2010, pp. 3501-3508.
36. M. Yang, D. Dai, L. Shen, L.V. Gool, Latent dictionary learning for sparse representation based classification, In: Computer Vision and Pattern Recognition, IEEE, Columbus, 2014, pp. 4138-4145.
37. Q. Qiu, Z. Jiang, Sparse dictionary-based representation and recognition of action attributes, In: International Conference on Machine Learning, IEEE, Bellevue, Washington, 2011, pp. 704-714.
38. J.C. Gemert, J.-M. Geusebroek, C.J. Veenman, A.W. Smeulders, Kernel codebooks for scene categorization, In: European Conference on Computer Vision, Springer-Verlag, Marseille, 2008, pp. 696-709.
39. L. Liu, L. Wang, X. Liu, In defense of soft-assignment coding, In: International Conference on Computer Vision, IEEE, Barcelona, 2011, pp. 2486-2493.
40. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, Y. Gong, Locality-constrained linear coding for image classification, In: Computer Vision and Pattern Recognition, IEEE, Colorado Springs, 2010, pp. 3360-3367.
41. Y. Huang, K. Huang, Y. Yu, T. Tan, Salient coding for image classification, In: Computer Vision and Pattern Recognition, IEEE, San Francisco, 2011, pp. 1036-6919.
42. D.H. Hubel, and T.N. Wiesel Receptive fields, binocular interaction and functional architecture in the cat's visual cortex J. Physiol. 160 1 1962 106
43. Y.-L. Boureau, N. Le Roux, F. Bach, J. Ponce, Y. LeCun, Ask the locals: multi-way local pooling for image recognition, In: International Conference on Computer Vision, IEEE, Barcelona, 2011, pp. 2651-2658.
44. Y.-L. Boureau, F. Bach, Y. LeCun, J. Ponce, Learning mid-level features for recognition, In: Computer Vision and Pattern Recognition, IEEE, Colorado Springs, 2010, pp. 2559-2566.
45. J. Zhu, T. Wu, S.-C. Zhu, X. Yang, W. Zhang, Learning reconfigurable scene representation by tangram model, In: IEEE Winter Conference on Applications of Computer Vision, IEEE, Breckenridge, Colorado, 2012, pp. 449-456.
46. J. Zhu, W. Zou, X. Yang, R. Zhang, Q. Zhou, W. Zhang, Image classification by hierarchical spatial pooling with partial least squares analysis, In: British Machine Vision Conference, BMVA Press, University of Surrey, 2012, pp. 102.1-102.11.
47. L.-J. Li, F.-F. Li, What, where and who? Classifying events by scene and object recognition, In: International Conference on Computer Vision, IEEE, Rio de Janeiro, 2007, pp. 1-8.
48. Y. Yang, S. Newsam, Spatial pyramid co-occurrence for image classification, In: International Conference on Computer Vision, Barcelona, IEEE, 2011, pp. 1465-1472.
49. F.-F. Li, P. Perona, A Bayesian hierarchical model for learning natural scene categories, In: Computer Vision and Pattern Recognition, vol. 2, IEEE, San Diego, 2005, pp. 524-531.
50. A. Vedaldi, and A. Zisserman Efficient additive kernels via explicit feature maps IEEE Trans. Pattern Anal. Mach. Intell. 34 3 2012 480 492
51. K. Grauman, T. Darrell, The pyramid match kernel: Discriminative classification with sets of image features, In: International Conference on Computer Vision, vol. 2, IEEE, Beijing, 2005, pp. 1458-1465.
52. M. MarsZalek, C. Schmid, H. Harzallah, v.d.W. Joost, Learning object representations for visual object class recognition, In: Visual Recognition Challenge Workshop, 2007.