Cited By
View all- Jaiswal SEgede JValstar M(2018)Deep Learned Cumulative Attribute Regression2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)10.1109/FG.2018.00113(715-722)Online publication date: 15-May-2018
Spectral attribute learning for visual regression
Abstract
A number of computer vision problems such as facial age estimation, crowd counting and pose estimation can be solved by learning regression mapping on low-level imagery features. We show that visual regression can be substantially improved by two-stage regression where imagery features are first mapped to an attribute space which explicitly models latent correlations across continuously-changing output. We propose an approach to automatically discover spectral attributes which avoids manual work required for defining hand-crafted attribute representations. Visual attribute regression outperforms direct visual regression and our spectral attribute visual regression achieves state-of-the-art accuracy in multiple applications. HighlightsSpectral attributes avoid manually-engineered attribute construction.Spectral attributes handle multiple correlated regression outputs.Spectral attributes achieve state-of-the-art performance on various benchmarks.
References
[1]
X. Geng, Z.-H. Zhou, K. Smith-Miles, Automatic age estimation based on facial aging patterns, IEEE Trans. Pattern Anal. Mach. Intell., 29 (2007) 2234-2240.
[2]
Y. Fu, G. Guo, T.S. Huang, Age synthesis and estimation via faces: a survey, IEEE Trans. Pattern Anal. Mach. Intell., 32 (2010) 1955-1976.
[3]
Y. Zhang, D. Yeung, Multi-tasks warped Gaussian process for personalized age estimation, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2010.
[4]
K. Chen, S. Gong, T. Xiang, C.C. Loy, Cumulative attribute space for age and crowd density estimation, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2013.
[5]
A.B. Chan, N. Vasconcelos, Counting people with low-level features and Bayesian regression, IEEE Trans. Image Process., 21 (2012) 2160-2177.
[6]
G. Guo, Y. Fu, C. Dyer, T. Huang, Head pose estimation: Classification or regression?, in: Proceedings of International Conference on Pattern Recognition, 2008.
[7]
S. Yan, X. Zhou, M. Liu, M. Hasegawa-Johnson, T. Huang, Regression from patch-kernel, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[8]
E. Murphy-Chutorian, M.M. Trivedi, Head pose estimation in computer vision: a survey, IEEE Trans. Pattern Anal. Mach. Intell., 31 (2009) 607-626.
[9]
G. Mu, G. Guo, Y. Fu, T.S. Huang, Human age estimation using bio-inspired features, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 112119.
[10]
S. An, W. Liu, S. Venkatesh, Face recognition using kernel ridge regression, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007.
[11]
V. Ferrari, A. Zisserman, Learning visual attributes, in: Proceedings of Advances in Neural Information Processing Systems, 2007.
[12]
C.H. Lampert, H. Nickisch, S. Harmeling, Learning to detect unseen object classes by between-class attribute transfer, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009.
[13]
Y. Fu, T.M. Hospedales, T. Xiang, S. Gong, Attribute learning for understanding unstructured social activity, in: Proceedings of European Conference on Computer Vision, 2012.
[14]
K. Kamvar, S. Sepandar, K. Klein, D. Dan, M. Manning, C. Christopher, Spectral learning, in: Proceedings of International Joint Conference of Artificial Intelligence, 2003.
[15]
K.-Y. Chang, C.-S. Chen, Y.-P. Hung, Ordinal hyperplanes ranker with cost sensitivities for age estimation, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2011.
[16]
X. Geng, C. Yin, Z.-H. Zhou, Facial age estimation by learning from label distributions, IEEE Trans. Pattern Anal. Mach. Intell., 35 (2013) 2401-2412.
[17]
D. Parikh, K. Grauman, Relative attributes, in: Proceedings of International Conference on Computer Vision, 2011.
[18]
G. Patterson, J. Hays, SUN attribute database: Discovering, annotating, and recognizing scene attributes, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2012.
[19]
T.L. Berg, A.C. Berg, J. Shih, Automatic attribute discovery and characterization from noisy web data, in: Proceedings of European Conference on Computer Vision, 2010.
[20]
T. Joachims, Optimizing search engines using clickthrough data, in: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002.
[21]
I. Endres, D. Hoiem, Category independent object proposals, in: Proceedings of European Conference on Computer Vision, 2010.
[22]
M. Kim, V. Pavlovic, Structured output ordinal regression for dynamic facial emotion intensity prediction, in: Proceedings of European Conference on Computer Vision, 2010.
[23]
W. Chen, C. Xiong, R. Xu, J.J. Corso, Actionness ranking with lattice conditional ordinal random fields, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014.
[24]
T. Malisiewicz, A. Gupta, A. Efros, Ensemble of exemplar-SVMs for object detection and beyond, in: Proceedings of International Conference on Computer Vision, 2011.
[25]
A.C. Berg, T.L. Berg, J. Malik, Shape matching and object recognition using low distortion correspondences, in: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.
[26]
S. Bagon, O. Brostovski, M. Galun, M. Irani, Detecting and sketching the common, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2010.
[27]
M. Torki, A. Elgammal, One-shot multi-set non-rigid feature-spatial matching, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2010.
[28]
A. Ng, M. Jordan, Y. Weiss, On spectral clustering: Analysis and an algorithm, in: Proceedings of Advances in neural information processing systems, 2002.
[29]
S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2006.
[30]
L. Zelnik-manor, P. Perona, Self-tuning spectral clustering, in: Proceedings of Advances in Neural Information Processing Systems, 2005.
[31]
D. Tao, L. Jin, Y. Wang, Y. Yuan, X. Li, Person re-identification by regularized smoothing kiss metric learning, IEEE Trans. Circuits Syst. Video Technol., 23 (2013) 1675-1685.
[32]
D. Tao, Y. Guo, M. Song, Y. Li, Z. Yu, Y.Y. Tang, Person re-identification by dual-regularized kiss metric learning, IEEE Trans. Image Process., 25 (2016) 2726-2738.
[33]
D. Tao, L. Jin, W. Liu, X. Li, Hessian regularized support vector machines for mobile image annotation on the cloud, IEEE Trans. Multimed., 15 (2013) 833-844.
[34]
L. Yang, S. Yang, S. Li, R. Zhang, F. Liu, L. Jiao, Coupled compressed sensing inspired sparse spatial-spectral lssvm for hyperspectral image classification, Knowl.-Based Syst., 79 (2015) 80-89.
[35]
R. Shang, Z. Zhang, L. Jiao, C. Liu, Y. Li, Self-representation based dual-graph regularized feature selection clustering, Neurocomputing, 171 (2016) 1242-1253.
[36]
R. Shang, Z. Zhang, L. Jiao, W. Wang, S. Yang, Global discriminative-based nonnegative spectral clustering, Pattern Recognit., 55 (2016) 172-182.
[37]
L. Jiao, F. Shang, F. Wang, Y. Liu, Fast semi-supervised clustering with enhanced spectral embedding, Pattern Recognit., 45 (2012) 4358-4369.
[38]
T. Jaakkola, D. Haussler, Exploiting generative models in discriminative classifiers, in: Proceedings of Advances in neural information processing systems, 1998.
[39]
J. Sanchez, F. Perronnin, T. Mensink, J. Verbeek, Image classification with the Fisher vector: theory and practice, Int. J. Comput. Vis., 105 (2013) 222-245.
[40]
K. Chatfield, V. Lempitsky, A. Vedaldi, A. Zisserman, The devil is in the details: an evaluation of recent feature encoding methods, in: Proceedings of British Machine Vision Conference, 2011.
[41]
K. Chen, L. Zhang, Y. Zhang, Cyclic motion generation of multi-link planar robot performing square end-effector trajectory analyzed via gradient-descent and zhang et als neural-dynamic methods, in: ISSCAA, 2008.
[42]
A. Argyriou, T. Evgeniou, M. Pontil, Multi-task feature learning, in: NIPS, 2006.
[43]
A. Argyriou, T. Evgeniou, M. Pontil, A. Argyriou, T. Evgeniou, M. Pontil, Convex multi-task feature learning, Mach. Learn., 73 (2008) 243-272.
[44]
Z. Kang, K. Grauman, F. Sha, Learning with whom to share in multi-task feature learning, in: Proceedings of International Conference on Machine Learning, 2011.
[45]
M. Solnon, S. Arlot, F. Bach, Multi-task regression using minimal penalties, JMLR.
[46]
S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers, Found. Trends Mach. Learn.
[47]
H. Borchani, G. Varando, C. Bielza, P. Larraaga, A survey on multi-output regression, Wiley Interdisciplinary Reviews, Data Min. Knowl. Discov., 5 (2015) 216-233.
[48]
A.J. Smola, B. Schlkopf, A tutorial on support vector regression, Stat. Comput., 14 (2004) 199-222.
[49]
T. Joachims, T. Finley, C.-N.J. Yu, Cutting-plane training of structural svms, Mach. Learn., 77 (2009) 27-59.
[50]
A.B. Chan, Z.-S. J. Liang, N. Vasconcelos, Privacy preserving crowd monitoring: counting people without people models or tracking, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[51]
K. Chen, C.C. Loy, S. Gong, T. Xiang, Feature mining for localised crowd counting, in: Proceedings of British Machine Vision Conference, 2012.
[52]
N. Gourier, D. Hall, J.L. Crowley, Estimating face orientation from robust detection of salient facial structures, in: Proceedings of International Conference on Pattern Recognition, 2004.
[53]
C.C. Loy, S. Gong, T. Xiang, From semi-supervised to transfer counting of crowds, in: Proceedings of International Conference on Computer Vision, 2013.
[54]
T.F. Cootes, G.J. Edwards, C.J. Taylor, Active appearance models, IEEE Trans. Pattern Anal. Mach. Intell., 23 (2001) 681-685.
[55]
S. Yan, H. Wang, X. Tang, T.S. Huang, Learning auto-structured regressor from uncertain nonnegative labels, in: Proceedings of International Conference on Computer Vision, 2007.
[56]
G. Guo, Y. Fu, C.R. Dyer, T.S. Huang, Image-based human age estimation by manifold learning and locally adjusted robust regression, IEEE Trans. Image Process., 17 (2008) 1178-1188.
[57]
N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: Proceedings of IEEE Computer Vision and Pattern Recognition, 2005.
[58]
X. Geng, Y. Xia, Head pose estimation based on multivariate label distribution, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2014.
[59]
K. Hara, R. Chellappa, Growing regression forests by classification: Applications to object pose estimation, in: Proceedings of European Conference on Computer Vision, 2014.
[60]
S. Yan, H. Wang, T.S. Huang, Q. Yang, X. Tang, Ranking with uncertain labels, in: Proceedings of IEEE International Conference on Multimedia and Expo, 2007.
[61]
K.-Y. Chang, C.-S. Chen, Y.-P. Hung, A ranking approach for human ages estimation based on face images, in: Proceedings of International Conference on Pattern Recognition, 2010.
[62]
K. Chen, J.-K. Kmrinen, Learning to count with back-propagated information, in: Proceedings of International Conference on Pattern Recognition, 2014.
[63]
M.A. Haj, J. Gonzalez, L.S. Davis, On partial least squares in head pose estimation: How to simultaneously deal with misalignment, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2012.
[64]
K. Simonyan, A. Vedaldi, A. Zisserman, Deep Fisher networks for large-scale image classification, in: Proceedings of Advances in Neural Information Processing systems, 2013.
- Spectral attribute learning for visual regression
Recommendations
Using locally weighted learning to improve SMOreg for regression
PRICAI'06: Proceedings of the 9th Pacific Rim international conference on Artificial intelligenceShevade et al.[1] are successful in extending some improved ideas to Smola and Scholkopf's SMO algorithm[2] for solving regression problems, simply named SMOreg. In this paper, we use SMOreg in exactly the same way as linear regression(LR) is used in ...
Attribute Dominance: What Pops Out?
ICCV '13: Proceedings of the 2013 IEEE International Conference on Computer VisionWhen we look at an image, some properties or attributes of the image stand out more than others. When describing an image, people are likely to describe these dominant attributes first. Attribute dominance is a result of a complex interplay between the ...
Finding the Best Regression Subset by Reduction in Nonfull-Rank Cases
The computational problem of finding the best fitting subset of independent variables in least-squares regression with a fixed subset size is addressed, especially in the context of the nonfull-rank case with more variables than observations. For the ...
Comments
Information & Contributors
Information
Published In
Copyright © Elsevier Ltd.
Publisher
Elsevier Science Inc.
United States
Publication History
Published: 01 June 2017
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 18 Jan 2025
Other Metrics
Citations
Cited By
View all- Jaiswal SEgede JValstar M(2018)Deep Learned Cumulative Attribute Regression2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)10.1109/FG.2018.00113(715-722)Online publication date: 15-May-2018