AID: A benchmark data set for performance evaluation of aerial scene classification

IEEE Transactions on Geoscience and Remote Sensing

Volumn 55, Issue 7, 2017, Pages 3965-3981

  Xia, Gui Song ^a   Hu, Jingwen ^a   Hu, Fan ^a   Shi, Baoguang ^b   Bai, Xiang ^b   Zhong, Yanfei ^a   Zhang, Liangpei ^a   Lu, Xiaoqiang ^c  

^a WUHAN UNIVERSITY   (China)

^b HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^c XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

Author keywords

Aerial images; Benchmark; Scene classification

Indexed keywords

BENCHMARKING; DEEP LEARNING; IMAGE RECONSTRUCTION; LEARNING SYSTEMS; REMOTE SENSING; SEMANTICS;

AERIAL IMAGE DATA; DATA-DRIVEN APPROACH; HIGH RESOLUTION REMOTE SENSING IMAGERY; LARGE SCALE DATA SETS; LEARNING APPROACH; PERFORMANCE ANALYSIS; SCENE CLASSIFICATION; SPECIFIC SEMANTICS;

CLASSIFICATION (OF INFORMATION);

AERIAL SURVEY; ALGORITHM; AUTOMATION; BENCHMARKING; DATA SET; IMAGE CLASSIFICATION; MACHINE LEARNING; PERFORMANCE ASSESSMENT; REMOTE SENSING;

EID: 85018642692     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2017.2685945     Document Type: Article

References (100)

1. Q. Hu et al., "Exploring the use of Google earth imagery and objectbased methods in land use/cover mapping," Remote Sens., vol. 5, no. 11, pp. 6026-6042, 2013.
2. G. Cheng, J. Han, L. Guo, Z. Liu, S. Bu, and J. Ren, "Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 8, pp. 4238-4249, Aug. 2015.
3. G. Cheng, J. Han, P. Zhou, and L. Guo, "Multi-class geospatial object detection and geographic image classification based on collection of part detectors," ISPRS J. Photogramm. Remote Sens., vol. 98, pp. 119-132, Dec. 2014.
4. F. Hu, G.-S. Xia, J. Hu, and L. Zhang, "Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery," Remote Sens., vol. 7, no. 11, pp. 14680-14707, 2015.
5. G. Cheng and J. Han, "A survey on object detection in optical remote sensing images," ISPRS J. Photogram. Remote Sens., vol. 117, pp. 11-28, Jul. 2016.
6. J. Han, D. Zhang, G. Cheng, L. Guo, and J. Ren, "Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 6, pp. 3325-3337, Jun. 2015.
7. V. Risojević and Z. Babić, "Aerial image classification using structural texture similarity," in Proc. IEEE Int. Symp. Signal Process. Inf. Technol. (ISSPIT), Dec. 2011, pp. 190-195.
8. Y. Yang and S. Newsam, "Spatial pyramid co-occurrence for image classification," in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Nov. 2011, pp. 1465-1472.
9. G. Sheng, W. Yang, T. Xu, and H. Sun, "High-resolution satellite scene classification using a sparse coding based multiple feature combination," Int. J. Remote Sens., vol. 33, no. 8, pp. 2395-2412, 2012.
10. V. Risojević and Z. Babić, "Orientation difference descriptor for aerial image classification," in Proc. Int. Conf. Syst., Signals Image Process. (IWSSIP), Apr. 2012, pp. 150-153.
11. F. Hu, W. Yang, J. Chen, and H. Sun, "Tile-level annotation of satellite images using multi-level max-margin discriminative random field," Remote Sens., vol. 5, no. 5, pp. 2275-2291, 2013.
12. B. Luo, S. Jiang, and L. Zhang, "Indexing of remote sensing images with different resolutions by multiple features," IEEE J. Sel. Topics Appl. Earth Observat. Remote Sens., vol. 6, no. 4, pp. 1899-1912, Aug. 2013.
13. W. Shao, W. Yang, G.-S. Xia, and G. Liu, "A hierarchical scheme of multiple feature fusion for high-resolution satellite scene categorization," in Computer Vision Systems. Berlin, Germany: Springer, 2013, pp. 324-333.
14. W. Shao, W. Yang, and G.-S. Xia, "Extreme value theory-based calibration for the fusion of multiple features in high-resolution satellite scene classification," Int. J. Remote Sens., vol. 34, no. 23, pp. 8588-8602, 2013.
15. V. Risojević and Z. Babić, "Fusion of global and local descriptors for remote sensing image classification," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 4, pp. 836-840, Jul. 2013.
16. Y. Yang and S. Newsam, "Geographic image retrieval using local invariant features," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 2, pp. 818-832, Feb. 2013.
17. B. Zhao, Y. Zhong, and L. Zhang, "Scene classification via latent Dirichlet allocation using a hybrid generative/discriminative strategy for high spatial resolution remote sensing imagery," Remote Sens. Lett., vol. 4, no. 12, pp. 1204-1213, 2013.
18. X. Zheng, X. Sun, K. Fu, and H. Wang, "Automatic annotation of satellite images via multifeature joint sparse coding with spatial relation constraint," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 4, pp. 652-656, Jul. 2013.
19. A. Avramović and V. Risojević, "Block-based semantic classification of high-resolution multispectral aerial images," Signal, Image Video Process., vol. 10, no. 1, pp. 75-84, Jan. 2016.
20. A. M. Cheriyadat, "Unsupervised feature learning for aerial scene classification," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 1, pp. 439-451, Jan. 2014.
21. R. Kusumaningrum, H. Wei, R. Manurung, and A. Murni, "Integrated visual vocabulary in latent Dirichlet allocation-based scene classification for IKONOS image," J. Appl. Remote Sens., vol. 8, no. 1, p. 083690, 2014.
22. R. Negrel, D. Picard, and P.-H. Gosselin, "Evaluation of second-order visual features for land-use classification," in Proc. Int. Workshop Content-Based Multimedia Indexing (CBMI), Jun. 2014, pp. 1-5.
23. L. Zhao, P. Tang, and L. Huo, "A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification," Int. J. Remote Sens., vol. 35, no. 6, pp. 2296-2310, 2014.
24. L.-J. Zhao, P. Tang, and L.-Z. Huo, "Land-use scene classification using a concentric circle-structured multiscale bag-of-visual-words model," IEEE J. Sel. Topics Appl. Earth Observat. Remote Sens., vol. 7, no. 12, pp. 4620-4631, Dec. 2014.
25. Q. Zhu, Y. Zhong, and L. Zhang, "Multi-feature probability topic scene classifier for high spatial resolution remote sensing imagery," in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Jul. 2014, pp. 2854-2857.
26. S. Chen and Y. Tian, "Pyramid of spatial relatons for scene-level land use classification," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 4, pp. 1947-1957, Apr. 2015.
27. X. Chen, T. Fang, H. Huo, and D. Li, "Measuring the effectiveness of various features for thematic information extraction from very high resolution remote sensing imagery," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 9, pp. 4837-4851, Sep. 2015.
28. Y. Zhong, M. Cui, Q. Zhu, and L. Zhang, "Scene classification based on multifeature probabilistic latent semantic analysis for high spatial resolution remote sensing images," J. Appl. Remote Sens., vol. 9, no. 1, p. 095064, 2015.
29. H. Sridharan and A. Cheriyadat, "Bag of lines (BoL) for improved aerial scene representation," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 3, pp. 676-680, Mar. 2015.
30. J. Hu, G.-S. Xia, F. Hu, and L. Zhang, "A comparative study of sampling analysis in the scene classification of optical high-spatial resolution remote sensing imagery," Remote Sens., vol. 7, no. 11, pp. 14988-15013, 2015.
31. J. Hu, T. Jiang, X. Tong, G.-S. Xia, and L. Zhang, "A benchmark for scene classification of high spatial resolution remote sensing imagery," in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Jul. 2015, pp. 5003-5006.
32. F. Hu, G.-S. Xia, Z. Wang, X. Huang, L. Zhang, and H. Sun, "Unsupervised feature learning via spectral clustering of multidimensional patches for remotely sensed scene classification," IEEE J. Sel. Topics Appl. Earth Observat. Remote Sens., vol. 8, no. 5, pp. 2015-2030, May 2015.
33. M. Castelluccio, G. Poggi, C. Sansone, and L. Verdoliva. (2015). "Land use classification in remote sensing images by convolutional neural networks." [Online]. Available: Https://arxiv.org/abs/1508.00092
34. O. A. B. Penatti, K. Nogueira, and J. A. dos Santos, "Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?" in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 44-51.
35. F. P. S. Luus, B. Salmon, F. van den Bergh, and B. T. J. Maharaj, "Multiview deep learning for land-use classification," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 12, pp. 2448-2452, Dec. 2015.
36. W. Yang, X. Yin, and G. S. Xia, "Learning high-level features for satellite image classification with limited labeled samples," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 8, pp. 4472-4482, Aug. 2015.
37. F. Zhang, B. Du, and L. Zhang, "Saliency-guided unsupervised feature learning for scene classification," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 4, pp. 2175-2184, Apr. 2015.
38. F. Zhang, B. Du, and L. Zhang, "Scene classification via a gradient boosting random convolutional network framework," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 3, pp. 1793-1802, Mar. 2016.
39. Y. Zhong, Q. Zhu, and L. Zhang, "Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 11, pp. 6207-6222, Nov. 2015.
40. C. Chen, B. Zhang, H. Su, W. Li, and L. Wang, "Land-use scene classification using multi-scale completed local binary patterns," Signal, Image Video Process., vol. 10, no. 4, pp. 745-752, Apr. 2016.
41. Q. Zou, L. Ni, T. Zhang, and Q. Wang, "Deep learning based feature selection for remote sensing scene classification," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 11, pp. 2321-2325, Nov. 2015.
42. K. Nogueira, O. A. Penatti, and J. A. dos Santos. (2016). "Towards better exploiting convolutional neural networks for remote sensing scene classification." [Online]. Available: Https://arxiv.org/abs/1602.01517
43. D. Tuia, F. Ratle, F. Pacifici, M. F. Kanevski, and W. J. Emery, "Active learning methods for remote sensing image classification," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 7, pp. 2218-2232, Jul. 2009.
44. D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Muñoz-Marí, "A survey of active learning algorithms for supervised remote sensing image classification," IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 606-617, Jun. 2011.
45. T. Blaschke and J. Strobl, "What's wrong with pixels? Some recent developments interfacing remote sensing and GIS," GeoBIT/GIS, vol. 6, no. 1, pp. 12-17, 2001.
46. N. B. Kotliar and J. A. Wiens, "Multiple scales of patchiness and patch structure: A hierarchical framework for the study of heterogeneity," Oikos, vol. 59, no. 2, pp. 253-260, 1990.
47. T. Blaschke, "Object-based contextual image classification built on image segmentation," in Proc. IEEE Workshop Adv. Techn. Anal. Remotely Sensed Data, Oct. 2003, pp. 113-119.
48. Y. Gao, J.-F. Mas, B. Maathuis, X. Zhang, and P. M. Van Dijk, "Comparison of pixel-based and object-oriented image classification approaches-A case study in a coal fire area, Wuda, Inner Mongolia, China," Int. J. Remote. Sens., vol. 27, no. 18, pp. 4039-4055, 2006.
49. T. Blaschke, "Object based image analysis for remote sensing," ISPRS J. Photogram. Remote Sens., vol. 65, no. 1, pp. 2-16, Jan. 2010.
50. S. W. Myint, P. Gober, A. Brazel, S. Grossman-Clarke, and Q. Weng, "Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery," Remote Sens. Environ., vol. 115, no. 5, pp. 1145-1161, 2011.
51. D. C. Duro, S. E. Franklin, and M. G. Dubé, "A comparison of pixelbased and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery," Remote Sens. Environ., vol. 118, pp. 259-272, Mar. 2012.
52. Y. Zhong, J. Zhao, and L. Zhang, "A hybrid object-oriented conditional random field classification framework for high spatial resolution remote sensing imagery," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 11, pp. 7023-7037, Nov. 2014.
53. J. Zhao, Y. Zhong, and L. Zhang, "Detail-preserving smoothing classifier based on conditional random fields for high spatial resolution remote sensing imagery," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2440-2452, May 2015.
54. Y. Yang and S. Newsam, "Comparing SIFT descriptors and Gabor texture features for classification of remote sensed imagery," in Proc. IEEE Int. Conf. Image Process., Oct. 2008, pp. 1852-1855.
55. J. A. dos Santos, O. A. B. Penatti, and R. da Silva Torres, "Evaluating the potential of texture and color descriptors for remote sensing image retrieval and classification," in Proc. VISAPP, May 2010, pp. 203-208.
56. M. Liénou, H. Maître, and M. Datcu, "Semantic annotation of satellite images using latent Dirichlet allocation," IEEE Geosci. Remote Sens. Lett., vol. 7, no. 1, pp. 28-32, Jan. 2010.
57. G.-S. Xia, W. Yang, J. Delon, Y. Gousseau, H. Sun, and H. Maître, "Structural high-resolution satellite image indexing," in Proc. ISPRS TC 7th Symp. Years ISPRS, vol. 38. 2010, pp. 298-303.
58. Y. Yang and S. Newsam, "Bag-of-visual-words and spatial extensions for land-use classification," in Proc. 18th SIGSPATIAL Int. Conf. Adv. Geogr. Inf. Syst., 2010, pp. 270-279.
59. L. Chen, W. Yang, K. Xu, and T. Xu, "Evaluation of local features for scene classification using vhr satellite images," in Proc. Joint Urban Remote Sens. Event (JURSE), Apr. 2011, pp. 385-388.
60. D. Dai and W. Yang, "Satellite image classification via two-layer sparse coding with biased image representation," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 1, pp. 173-176, Jan. 2011.
61. V. Risojević, S. Momić, and Z. Babić, "Gabor descriptors for aerial image classification," in Adaptive and Natural Computing Algorithms. Berlin, Germany: Springer, 2011, pp. 51-60.
62. B. Zhao, Y. Zhong, and L. Zhang, "Hybrid generative/discriminative scene classification strategy based on latent Dirichlet allocation for high spatial resolution remote sensing imagery," in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Jul. 2013, pp. 196-199.
63. A. Oliva and A. Torralba, "Modeling the shape of the scene: A holistic representation of the spatial envelope," Int. J. Comput. Vis., vol. 42, no. 3, pp. 145-175, 2001.
64. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.
65. G.-S. Xia, J. Delon, and Y. Gousseau, "Accurate junction detection and characterization in natural images," Int. J. Comput. Vis., vol. 106, no. 1, pp. 31-56, Jan. 2014.
66. M. J. Swain and D. H. Ballard, "Color indexing," Int. J. Comput. Vis., vol. 7, no. 1, pp. 11-32, 1991.
67. B. S. Manjunath and W. Y. Ma, "Texture features for browsing and retrieval of image data," IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 8, pp. 837-842, Aug. 1996.
68. G.-S. Xia, J. Delon, and Y. Gousseau, "Shape-based invariant texture indexing," Int. J. Comput. Vis., vol. 88, no. 3, pp. 382-403, Jul. 2010.
69. G. Liu, G.-S. Xia, W. Yang, and L. Zhang, "Texture analysis with shape co-occurrence patterns," in Proc. 22nd Int. Conf. Pattern Recognit., Stockholm, Sweden, Aug. 2014, pp. 1627-1632.
70. T. Ojala, M. Pietikäinen, and T. Mäenpää, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971-987, Jul. 2002.
71. B. Luo, J. F. Aujol, Y. Gousseau, and S. Ladjal, "Indexing of satellite images with different resolutions by wavelet features," IEEE Trans. Image Process., vol. 17, no. 8, pp. 1465-1472, Aug. 2008.
72. B. Luo, J.-F. Aujol, and Y. Gousseau, "Local scale measure from the topographic map and application to remote sensing images," Multiscale Model. Simul., vol. 8, no. 1, pp. 1-29, 2009.
73. S. Mallat and L. Sifre, "Combined scattering for rotation invariant texture analysis," in Proc. ESANN, Apr. 2012, pp. 68-81.
74. W. J. Scheirer, N. Kumar, P. N. Belhumeur, and T. E. Boult, "Multiattribute spaces: Calibration for attribute fusion and similarity search," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 2933-2940.
75. J. Yang, K. Yu, Y. Gong, and T. Huang, "Linear spatial pyramid matching using sparse coding for image classification," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2009, pp. 1794-1801.
76. F. Perronnin, J. Sánchez, and T. Mensink, "Improving the fisher kernel for large-scale image classification," in Proc. Eur. Conf. Comput. Vis., 2010, pp. 143-156.
77. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2. 2006, pp. 2169-2178.
78. D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent Dirichlet allocation," J. Mach. Learn. Res., vol. 3, pp. 993-1022, Mar. 2003.
79. M. Stricker and M. Orengo, "Similarity of color images," in Proc. IST/SPIE's Symp. Electron. Imag., Sci. Technol., 1995, pp. 381-392.
80. R. M. Haralick, K. Shanmugam, and I. H. Dinstein, "Textural features for image classification," IEEE Trans. Syst., Man, Cybern., Syst., vol. SMC-3, no. 6, pp. 610-621, Nov. 1973.
81. A. Bosch, A. Zisserman, and X. Muñoz, "Scene classification via pLSA," in Proc. Eur. Conf. Comput. Vis., 2006, pp. 517-530.
82. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion," J. Mach. Learn. Res., vol. 11, no. 12, pp. 3371-3408, Dec. 2010.
83. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1097-1105.
84. C. Szegedy et al. (2014). "Going deeper with convolutions." [Online]. Available: Https://arxiv.org/abs/1409.4842
85. K. Simonyan and A. Zisserman. (2014). "Very deep convolutional networks for large-scale image recognition." [Online]. Available: Https://arxiv.org/abs/1409.1556
86. B. Zhou, A. L. Garcia, J. Xiao, A. Torralba, and A. Oliva, "Learning deep features for scene recognition using places database," in Proc. Adv. Neural Inf. Process. Syst., vol. 1. 2015, pp. 487-495.
87. Y. Yuan, L. Mou, and X. Lu, "Scene recognition by manifold regularized deep learning architecture," IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 10, pp. 2222-2233, Oct. 2015.
88. G. Cheng, P. Zhou, and J. Han, "Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 12, pp. 7405-7415, Dec. 2016.
89. X. Yao, J. Han, G. Cheng, X. Qian, and L. Guo, "Semantic annotation of high-resolution satellite images via weakly supervised learning," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 6, pp. 3660-3671, Jun. 2016.
90. O. Russakovsky et al., "ImageNet large scale visual recognition challenge," Int. J. Comput. Vis., pp. 1-42, Apr. 2015.
91. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. (2013). "OverFeat: Integrated recognition, localization and detection using convolutional networks." [Online]. Available: Https://arxiv.org/abs/1312.6229
92. Y. Jia et al., "Caffe: Convolutional architecture for fast feature embedding," in Proc. ACM Int. Conf. Multimedia, 2014, pp. 675-678.
93. J. Sivic and A. Zisserman, "Video google: A text retrieval approach to object matching in videos," in Proc. IEEE Int. Conf. Comput. Vis., Oct. 2003, pp. 1470-1477.
94. H. Jégou, F. Perronnin, M. Douze, J. Sanchez, P. Perez, and C. Schmid, "Aggregating local image descriptors into compact codes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 9, pp. 1704-1716, Sep. 2012.
95. G. E. Hinton, S. Osindero, and Y. W. Teh, "A fast learning algorithm for deep belief nets," Neural Comput., vol. 18, no. 7, pp. 1527-1554, 2006.
96. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, "Localityconstrained linear coding for image classification," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp. 3360-3367.
97. K. Yu, T. Zhang, and Y. Gong, "Nonlinear learning using local coordinate coding," in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 2223-2231.
98. F. Perronnin and C. Dance, "Fisher kernels on visual vocabularies for image categorization," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2007, pp. 1-8.
99. L. Min, C. Qiang, and S. Yan. (2013). "Network in network." [Online]. Available: Https://arxiv.org/abs/1312.4400
100. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, "LIBLINEAR: A library for large linear classification," J. Mach. Learn. Res., vol. 9, pp. 1871-1874, Jun. 2008.