Utilizing Relevant RGB–D Data to Help Recognize RGB Images in the Target Domain
By: Depeng Gao, Jiafeng Liu, Rui Wu, Dansong Cheng, Xiaopeng Fan and Xianglong Tang
References
- Argyriou, A., Evgeniou, T. and Pontil, M. (2008). Convex multi-task feature learning, Machine Learning73(3): 243–272.
- Axler, S. (1997). Linear Algebra Done Right, Undergraduate Texts in Mathematics, Vol. 2, Springer, New York, NY.
- Belkin, M., Niyogi, P. and Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples, Journal of Machine Learning and Research7: 2399–2434.
- Bo, L., Ren, X. and Fox, D. (2013). Multipath sparse coding using hierarchical matching pursuit, 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, pp. 660–667.
- Chen, L., Li, W. and Xu, D. (2014). Recognizing RGB images by learning from RGB-D data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 1418–1425.
- Dai, W., Yang, Q., Xue, G.R. and Yu, Y. (2007). Boosting for transfer learning, International Conference on Machine Learning, Corvallis, FL, USA, pp. 193–200.
- Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K. and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, Miami, FL, USA, pp. 248–255.
- Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E. and Darrell, T. (2013). DeCAF: A deep convolutional activation feature for generic visual recognition, Proceedings of the 31st International Conference on Machine Learning, Beijing, China, pp. 647–655.
- Evgeniou, T. and Pontil, M. (2004). Regularized multi-task learning, 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA, pp. 109–117.
- Feyereisl, J. and Aickelin, U. (2012). Privileged information for data clustering, Information Sciences194: 4–23.
- Fouad, S., Tino, P., Raychaudhury, S. and Schneider, P. (2013). Incorporating privileged information through metric learning, IEEE Transactions on Neural Networks and Learning Systems24(7): 1086–1098.
- Gehler, P.V. and Nowozin, S. (2009). Let the kernel figure it out: Principled learning of pre-processing for kernel classifiers, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, pp. 2836–2843.
- Goswami, G., Vatsa, M. and Singh, R. (2014).RGB-D face recognition with texture and attribute features, IEEE Transactions on Information Forensics and Security9(10): 1629–1640.
- Griffin, G., Holub, A. and Perona, P. (2007). Caltech-256 object category dataset, California Institute of Technology, Pasadena, CA.
- Hadfield, S. and Bowden, R. (2013). Hollywood 3D: Recognizing actions in 3D natural scenes, IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, pp. 3398–3405.
- Huynh, T., Min, R. and Dugelay, J.L. (2012). An efficient LBP-based descriptor for facial depth images applied to gender recognition using RGB-D face data, Proceedings of the Asian Conference on Computer Vision, Tokyo, Japan, pp. 133–145.
- Janoch, A., Karayev, S., Jia, Y., Barron, J.T., Fritz, M., Saenko, K. and Darrell, T. (2013). A category-level 3d object dataset: Putting the kinect to work, in A. Fossati et al. (Eds), Consumer Depth Cameras for Computer Vision, Springer, London, pp. 141–165.
- Jiang, J. and Zhai, C.X. (2007). Instance weighting for domain adaptation in NLP, Meeting of the Association of Computational Linguistics, Prague, Czech Republic, pp. 264–271.
- Kovashka, A. and Grauman, K. (2010). Learning a hierarchy of discriminative space-time neighborhood features for human action recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, pp. 2046–2053.
- Kulis, B., Saenko, K. and Darrell, T. (2011). What you saw is not what you get: Domain adaptation using asymmetric kernel transforms, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA, pp. 1785–1792.
- Lai, K., Bo, L., Ren, X. and Fox, D. (2011). A large-scale hierarchical multi-view RGB-D object dataset, 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, pp. 1817–1824.
- LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning, Nature521(7553): 436–444.
- Li, W., Chen, L., Xu, D. and Gool, L.V. (2018). Visual recognition in RGB images and videos by learning from RGB-D data, IEEE Transactions on Pattern Analysis and Machine IntelligencePP(99): 1–1.
- Li, W., Duan, L., Xu, D. and Tsang, I.W. (2014). Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation, IEEE Transactions on Pattern Analysis and Machine Intelligence36(6): 1134–1148.
- Li, X., Fang, M., Zhang, J.-J. and Wu, J. (2017). Domain adaptation from RGB-D to RGB images, Signal Processing131: 27–35.
- Liu, J., Ji, S. and Ye, J. (2009). Multi-task feature learning via efficient l2,1-norm minimization, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, Montreal, Canada, pp. 339–348.
- Long, M., Wang, J., Ding, G., Pan, S.J. and Yu, P.S. (2014). Adaptation regularization: A general framework for transfer learning, IEEE Transactions on Knowledge and Data Engineering26(5): 1076–1089.
- Mihalkova, L., Huynh, T. and Mooney, R.J. (2007). Mapping and revising Markov logic networks for transfer learning, Proceedings of the 22nd AAAI Conference on Artificial Intelligence, Vancouver, Canada, pp. 608–614.
- Motiian, S. and Doretto, G. (2016). Information bottleneck domain adaptation with privileged information for visual recognition, Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands, pp. 630–647.
- Motiian, S., Piccirilli, M., Adjeroh, D.A. and Doretto, G. (2016). Information bottleneck learning using privileged information for visual recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 1496–1505.
- Nuricumbo, J.R., Ali, H., Mrton, Z.C. and Grzegorzek, M. (2015). Improving object classification robustness in RGB-D using adaptive SVMS, Multimedia Tools and Applications75(12): 1–19.
- Pan, S.J. and Yang, Q. (2010). A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering22(10): 1345–1359.
- Saenko, K., Kulis, B., Fritz, M. and Darrell, T. (2010). Adapting Visual Category Models to New Domains, Springer, Berlin/Heidelberg.
- Sharmanska, V., Quadrianto, N. and Lampert, C.H. (2013). Learning to rank using privileged information, Proceedings of the IEEE International Conference on Computer Vision, Portland, OR, USA, pp. 825–832.
- Sun, S. (2013). A survey of multi-view machine learning, Neural Computing and Applications23(7–8): 2031–2038.
- Vapnik, V. and Vashist, A. (2009). A new learning paradigm: Learning using privileged information, Neural Networks22(5): 544–557.
- Weiss, K., Khoshgoftaar, T.M. and Wang, D. (2016). A survey of transfer learning, Journal of Big Data3(1): 9.
- Xiao, Y., Wu, S.Y. and He, B.S. (2013). A proximal alternating direction method for l2,1-norm least squares problem in multi-task feature learning, Journal of Industrial and Management Optimization8(4): 1057–1069.
- Xu, Y., Pan, S.J., Xiong, H., Wu, Q., Luo, R., Min, H. and Song, H. (2017). A unified framework for metric transfer learning, IEEE Transactions on Knowledge and Data Engineering29(6): 1158–1171.
- Yang, J., Yan, R. and Hauptmann, A.G. (2007). Cross-domain video concept detection using adaptive SVMS, Proceedings of the 15th ACM International Conference on Multimedia, Augsburg, Germany, pp. 188–197.
- Yu, K. and Fu, Y. (2016). Discriminative relational representation learning for RGB-D action recognition, IEEE Transactions on Image Processing25(6): 2856–2865.
Language: English
Page range: 611 - 621
Submitted on: Nov 30, 2018
Accepted on: Apr 29, 2019
Published on: Sep 28, 2019
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
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© 2019 Depeng Gao, Jiafeng Liu, Rui Wu, Dansong Cheng, Xiaopeng Fan, Xianglong Tang, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.