Abstract
This work introduces semi-coupled transform learning. Given training data in two domains (source and target), it learns a transform in each of the domains such that the corresponding coefficients are (linearly) mapped from the source to the target. Since the mapping is in one direction (source to target) but not the other way round, we call it ‘semi-coupled’. Our work is the analysis equivalent of (semi) coupled dictionary learning. The proposed technique has been applied in two problems. The first being image super-resolution and the second, cross lingual document retrieval. In both the cases, our proposed transform learning based formulation excels considerably over existing techniques.
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References
Kim, K.I., Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE Trans. Pattern Anal. Mach. Intell. 32(6), 1127–1133 (2010)
Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)
Han, X.-H., Chen, Y.-W.: Sparse representation for image super-resolution. In: Chen, Y.-W., C. Jain, L. (eds.) Subspace Methods for Pattern Recognition in Intelligent Environment. SCI, vol. 552, pp. 123–150. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54851-2_6
Yang, J., Wang, Z., Lin, Z., Cohen, S., Huang, T.: Coupled dictionary training for image super-resolution. IEEE Trans. Image Process. 21(8), 3467–3478 (2012)
Wang, S., Zhang, L., Liang, Y., Pan, Q.: Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2216–2223 (2012)
Huang, D.A., Frank Wang, Y.C.: Coupled dictionary and feature space learning with applications to cross-domain image synthesis and recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2496–2503 (2013)
Gu, S., Zuo, W., Xie, Q., Meng, D., Feng, X., Zhang, L.: Convolutional sparse coding for image super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1823–1831 (2015)
Das, N., Mandal, D., Biswas, S.: Simultaneous semi-coupled dictionary learning for matching RGBD data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 243–251 (2016)
Mudunuri, S.P., Biswas, S.: A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching. Pattern Recogn. Lett. 71, 38–44 (2016)
Mehrotra, R., Agrawal, R., Haider, S.A.: Dictionary based sparse representation for domain adaptation. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 2395–2398 (2012)
Ravishankar, S., Bresler, Y.: Learning sparsifying transforms. IEEE Trans. Signal Process. 61(5), 1072–1086 (2013)
Ravishankar, S., Wen, B., Bresler, Y.: Online sparsifying transform learning-Part I: algorithms. J. Sel. Top. Signal Process. 9, 625–636 (2015)
Ravishankar, S., Bresler, Y.: Online sparsifying transform learning-Part II: convergence analysis. IEEE J. Sel. Top. Signal Process. 9(4), 637–646 (2015)
Ravishankar, S., Bresler, Y.: Efficient blind compressed sensing using sparsifying transforms with convergence guarantees and application to MRI. SIAM J. Imaging Sci. 8(4), 2519–2557 (2015)
Shekhar, S., Patel, V.M., Chellappa, R.: Analysis sparse coding models for image-based classification. In: IEEE International Conference on Image Processing (ICIP), pp. 5207–5211 (2014)
Maggu, J., Majumdar, A.: Alternate formulation for transform learning. In: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, p. 50. ACM (2016)
Maggu, J., Majumdar, A.: Robust transform learning. In: IEEE ICASSP, pp. 1467–1471 (2017)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311 (2006)
Platt, J.C., Toutanova, K.: Association for computational linguistics. In: Conference on Empirical Methods in Natural Language Processing, pp. 51–261 (2011)
Mimno, D., Wallach, H.M., Naradowsky, J., Smith, D.A., McCallum, A.: Polylingual topic models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 880–889 (2009)
Mehrotra, R., Chu, D., Haider, S.A., Kakadiaris, I.A.: Towards Learning Coupled Representations for Cross-Lingual Information Retrieval
Gupta, K., Bhowmick, B., Majumdar, A.: Motion blur removal via coupled autoencoder. In: IEEE International Conference on Image Processing (ICIP), pp. 480–484 (2017)
Gupta, K., Bhowmick, B., Majumdar, A.: Coupled analysis dictionary learning to inductively learn inversion: application to real-time reconstruction of biomedical signals. In: IEEE IJCNN 2018 (accepted)
Nagpal, S., Singh, M., Singh, R., Vatsa, M., Noore, A., Majumdar, A.: Face sketch matching via coupled deep transform learning, vol. 206, pp. 5429–5438 (2017)
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Maggu, J., Majumdar, A. (2018). Semi-coupled Transform Learning. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11303. Springer, Cham. https://doi.org/10.1007/978-3-030-04182-3_13
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DOI: https://doi.org/10.1007/978-3-030-04182-3_13
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