Cross Domains Image Processing via Fisher Linear Discriminative Analysis and Bregman Divergence

Document Type : Original Article

Authors

Faculty of IT & Computer Engineering, Urmia University of Technology, Urmia, Iran

Abstract

Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). In fact,the existence of conditional distribution difference across the source and target domains degrades the performance of model. Domain adaptation and transfer learning are promising solutions that aim to generalize a learning model across training and test data with different distributions. In this paper, we address the problem of unsupervised cross domain image processing in which no labels are available in test images. In fact, the proposed method transfers the source and target domains into a shared low dimensional FLDA-based subspace in an unsupervised manner. Our proposed method minimizes the conditional probability distribution difference of the source and target data via Bregman divergence. We provide a projection matrix to map the source and target data into a common subspace on which the between class scatter matrix is maximized and within class scatter matrix and cross domain distributions are minimized. Extensive experiments on 58 cross-domain image classification tasks over six public datasets reveal that our proposed method outperforms the state-of-the-art cross domain image processing approaches.

Keywords

References

[1]      B. Gong, K. Grauman and F. Sha, “Connecting the dots with landmarks: discriminatively learning domain-invariant features for unsupervised domain adaptation”, Proceedings of the International Conference on Machine Learning, vol. 28, no. 1, pp. 222-230, 2013.
[2]      H. Wang, H. Huang, F. Nie, and C. Ding, “Cross-language web page classification via dual knowledge transfer using nonnegative matrix tri-factorization,” in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp. 933–942, ACM, 2011.
[3]      J. Tahmoresnezhad and S. Hashemi, “Diret: An effective discriminative dimensionality reduction approach for multi source transfer learning,” Scientia Iranica. Transaction D, Computer Science & Engineering, Electrical, vol. 24, no. 3, pp. 1303–1311, 2017.
[4]      H. Liu and L. Yu, “Toward integrating feature selection algorithms for classification and clustering,” IEEE Transactions on knowledge and data engineering, vol. 17, no. 4, pp. 491502, 2005.
[5]      I. K. Fodor, “A survey of dimension reduction techniques,” Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, vol. 9, pp. 1–18, 2002.
[6]      L. M. Bregman, “The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming,” USSR computational mathematics and mathematical physics, vol. 7, no. 3, pp. 200–217, 1967.
[7]      مهرداد حیدری ارجلو، سید قدرت‌اله سیف السادات ومرتضی رزاز، «روش هوشمند تشخیص جزیره در شبکه توزیع دارای تولیدات پراکنده مبتنی بر تبدیل موجک و نزدیک‌ترین k-همسایگی (kNN)»، مجله مهندسی برق دانشگاه تبریز، جلد 43، شماره 1، صفحات 15-26، 1392.
[8]      M. Singha, D. Deb, and S. Roy, “Hybrid feature extraction method for partial face recognition,” Int. J. Emerg. Technol. Adv. Eng. Website, vol. 4, pp. 308–312, 2014.
[9]      Saenko K, Kulis B, Fritz M, Darrell T. Adapting visual category models to new domains. Computer Vision–ECCV 2010. 2010:213-26.
[10]      K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in European conference on computer vision, pp. 213–226, Springer, 2010.
[11]      M. Long, J. Wang, G. Ding, J. Sun and P. S. Yu, “Transfer joint matching for unsupervised domain adaptation”, IEEE conference on computer vision and pattern recognition, pp. 1410-1417, 2014.
[12]      J. Tahmoresnezhad and S. Hashemi, “Visual domain adaptation via transfer feature learning,” Knowledge and Information Systems, vol. 50, no. 2, pp. 585– 605, 2017.
[13]      M. Long, J. Wang, G. Ding, S. J. Pan and P. Yu, “Adaptation regularization: a general framework for transfer learning”, IEEE Trans. Knowl. Data Eng, vol. 26, pp. 1076–1089, 2013.
[14]      Y. Aytar and A. Zisserman, “Tabula rasa: Model transfer for object category detection,” in Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 2252–2259, IEEE, 2011.
[15]      G.Griffin, A. Holub and P. Perona, “Caltech-256 object category dataset”, Technical Report7694, 2007.
[16]      J. J. Hull, “A database for handwritten text recognition research”, IEEE Trans. Pattern Anal. Mach. Intell, vol. 16, no. 5, pp. 550–554, 1994.
[17]      Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, “Gradient-based learning applied to document recognition”, Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
[18]      T. Sim, S. Baker and M. Bsat, “The CMU pose, illumination, and expression (PIE) database”, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, pp. 53-58, 2002.
[19]      S. Si, D. Tao, and B. Geng, “Bregman divergence-based regularization for transfer subspace learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 7, p. 929, 2010.
[20]      L. Duan, D. Xu, and I. W.-H. Tsang, “Domain adaptation from multiple sources: A domaindependent regularization approach,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 3, pp. 504–518, 2012.
[21]      S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, “Domain adaptation via transfer component analysis,” IEEE Transactions on Neural Networks, vol. 22, no. 2, pp. 199–210, 2011.
[22]      M. Long, J. Wang, G. Ding, J. Sun and S. YuPhilip, “Transfer feature learning with joint distribution adaptation”, IEEE international conference on computer vision, pp. 2200-2207, 2013.
[23]      Y. Xu, X. Fang, J. Wu, X. Li, and D. Zhang, “Discriminative transfer subspace learning via low-rank and sparse representation,” IEEE Transactions on Image Processing, vol. 25, no. 2, pp. 850–863, 2016.
[24]      طاهره زارع بیدکی و محمدتقی صادقی، «بهینه‌سازی وزن‌ها در کرنل مرکب برای طبقه‌بند مبتنی بر نمایش تنک کرنلی»، مجله مهندسی برق دانشگاه تبریز، جلد 47، شماره 3، صفحات 1059-1072، 1396.
[25]      L. Luo, X. Wang, S. Hu, C. Wang, Y. Tang, and L. Chen, “Close yet distinctive domain adaptation,” IEEE Transactions on Image Processing, vol. 25, no. 2, pp. 850–863, 2017.
TABRIZ JOURNAL OF ELECTRICAL ENGINEERING
Volume 49, Issue 4 - Serial Number 90
February 2020
Pages 1681-1696

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