{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T15:16:47Z","timestamp":1761059807202},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"2-3","license":[{"start":{"date-parts":[[2014,10,2]],"date-time":"2014-10-02T00:00:00Z","timestamp":1412208000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2015,9]]},"DOI":"10.1007\/s11263-014-0757-x","type":"journal-article","created":{"date-parts":[[2014,10,1]],"date-time":"2014-10-01T11:53:51Z","timestamp":1412164431000},"page":"91-112","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Learning Sparse FRAME Models for Natural Image Patterns"],"prefix":"10.1007","volume":"114","author":[{"given":"Jianwen","family":"Xie","sequence":"first","affiliation":[]},{"given":"Wenze","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Song-Chun","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Ying Nian","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,10,2]]},"reference":[{"key":"757_CR1","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1207\/s15516709cog0901_7","volume":"9","author":"DH Ackley","year":"1985","unstructured":"Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive Science, 9, 147\u2013169.","journal-title":"Cognitive Science"},{"key":"757_CR2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/LSP.2012.2229705","volume":"20","author":"A Adler","year":"2013","unstructured":"Adler, A., Elad, M., & Hel-Or, Y. (2013). Probabilistic Subspace Clustering via Sparse Representations. IEEE Signal Processing Letters, 20, 63\u201366.","journal-title":"IEEE Signal Processing Letters"},{"key":"757_CR3","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","volume":"54","author":"M Aharon","year":"2006","unstructured":"Aharon, M., Elad, M., & Bruckstein, A. M. (2006). The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54, 4311\u20134322.","journal-title":"IEEE Transactions on Signal Processing"},{"key":"757_CR4","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Courville, A. C., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on PAMI, 35, 1798\u20131828.","DOI":"10.1109\/TPAMI.2013.50"},{"key":"757_CR5","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1137\/060657704","volume":"51","author":"AM Bruckstein","year":"2009","unstructured":"Bruckstein, A. M., Donoho, D. L., & Elad, M. (2009). From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Review, 51, 34\u201381.","journal-title":"SIAM Review"},{"key":"757_CR6","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1137\/S003614450037906X","volume":"43","author":"SS Chen","year":"2001","unstructured":"Chen, S. S., Donoho, D. L., & Saunders, M. A. (2001). Atomic decomposition by basis pursuit. SIAM Review, 43, 129\u2013159.","journal-title":"SIAM Review"},{"key":"757_CR7","unstructured":"Chen, J., & Huo, X. (2005). Sparse representations for multiple measurements vectors (mmv) in an overcomplete dictionary. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp. 257\u2013260."},{"key":"757_CR8","doi-asserted-by":"crossref","unstructured":"Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886-893.","DOI":"10.1109\/CVPR.2005.177"},{"key":"757_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum-likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B, 39, 1\u201338.","journal-title":"Journal of the Royal Statistical Society B"},{"key":"757_CR10","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/0370-2693(87)91197-X","volume":"195","author":"S Duane","year":"1987","unstructured":"Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters, 195, 216\u2013222.","journal-title":"Physics Letters"},{"key":"757_CR11","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4419-7011-4","volume-title":"Sparse and redundant representations: From theory to applications in signal and image processing","author":"M Elad","year":"2010","unstructured":"Elad, M. (2010). Sparse and redundant representations: From theory to applications in signal and image processing. Berlin: Springer."},{"key":"757_CR12","doi-asserted-by":"crossref","first-page":"3736","DOI":"10.1109\/TIP.2006.881969","volume":"15","author":"M Elad","year":"2006","unstructured":"Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions Image Processing, 15, 3736\u20133745.","journal-title":"IEEE Transactions Image Processing"},{"key":"757_CR13","doi-asserted-by":"crossref","unstructured":"Elad, M., Milanfar, P., & Rubinstein, R. (2007). Analysis versus synthesis in signal priors. Inverse problems, 23(3), 947.","DOI":"10.1088\/0266-5611\/23\/3\/007"},{"key":"757_CR14","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan, R. E., Chang, K. W., Hsieh, C. J., Wang, X. R., & Lin, C. J. (2008). LIBLINEAR: A library for large linear classication. Journal of Machine Learning Research, 9, 1871\u20131874.","journal-title":"Journal of Machine Learning Research"},{"key":"757_CR15","unstructured":"Fei-Fei, L., Fergus, R., & Perona, P. (2004). Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. In: Procedings of the Computer Vision and Pattern Recognition Workshops."},{"key":"757_CR16","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"P Felzenszwalb","year":"2010","unstructured":"Felzenszwalb, P., Girshick, R., McAllester, D., & Ramanan, D. (2010). Object detection with discriminatively trained part-based models. IEEE Transactions on PAMI, 32, 1627\u20131645.","journal-title":"IEEE Transactions on PAMI"},{"key":"757_CR17","doi-asserted-by":"crossref","unstructured":"Ferrari, V., Jurie, F., & Schmid, C. (2010). From images to shape models for object detection. International Journal of Computer Vision, 87, 284\u2013303.","DOI":"10.1007\/s11263-009-0270-9"},{"key":"757_CR18","doi-asserted-by":"crossref","unstructured":"Fidler, S., Boben, M. & Leonardis, A. (2008). Similarity-based cross-layered hierarchical representation for object categorization. In: Proceedings of the IEEE Conference on Computer Vision and PatternRecognition (CVPR).","DOI":"10.1109\/CVPR.2008.4587409"},{"key":"757_CR19","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1080\/01621459.1987.10478427","volume":"82","author":"JH Friedman","year":"1987","unstructured":"Friedman, J. H. (1987). Exploratory projection pursuit. Journal of the American Statistical Association, 82, 249\u2013266.","journal-title":"Journal of the American Statistical Association"},{"key":"757_CR20","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1214\/ss\/1028905934","volume":"13","author":"A Gelman","year":"1998","unstructured":"Gelman, A., & Meng, X. L. (1998). Simulating normalizing constants: from importance sampling to bridge sampling to path sampling. Statistical Science, 13, 163\u2013185.","journal-title":"Statistical Science"},{"key":"757_CR21","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/TPAMI.1984.4767596","volume":"6","author":"S Geman","year":"1984","unstructured":"Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Transactions on PAMI, 6, 721\u2013741.","journal-title":"IEEE Transactions on PAMI"},{"key":"757_CR22","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1090\/qam\/1939008","volume":"60","author":"S Geman","year":"2002","unstructured":"Geman, S., Potter, D. F., & Chi, Z. (2002). Composition systems. Quarterly of Applied Mathematics, 60, 707\u2013736.","journal-title":"Quarterly of Applied Mathematics"},{"key":"757_CR23","unstructured":"Gong, B., Shi, Y., Sha, F. & Grauman, K. (2012). Geodesic flow kernel for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"757_CR24","doi-asserted-by":"crossref","unstructured":"Gopalan, R., Li, R., & Chellappa, R. (2011). Domain adaptation for object recognition: an unsupervised approach. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV).","DOI":"10.1109\/ICCV.2011.6126344"},{"key":"757_CR25","unstructured":"Griffin, G., Holub, A., & Perona, P. (2007). Caltech-256 object category dataset. Caltech: Technical report."},{"key":"757_CR26","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1162\/089976602760128018","volume":"14","author":"GE Hinton","year":"2002","unstructured":"Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14, 1771\u20131800.","journal-title":"Neural Computation"},{"key":"757_CR27","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton, G. E., Osindero, S., & Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527\u20131554.","journal-title":"Neural Computation"},{"key":"757_CR28","unstructured":"Hoffman, J., Rodner, E., Donahue, J., Saenko, K., & Darrell, T. (2013). Efficient learning of domain-invariant image representations. In: Proceedings of the International Conference of Learning Representations."},{"key":"757_CR29","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1090\/S0033-569X-2013-01361-5","volume":"72","author":"Y Hong","year":"2013","unstructured":"Hong, Y., Si, Z., Hu, W., Zhu, S. C., & Wu, Y. N. (2013). Unsupervised learning of compositional sparse code for natural image representation. Quarterly of Applied Mathematics, 72, 373\u2013406.","journal-title":"Quarterly of Applied Mathematics"},{"key":"757_CR30","unstructured":"Jhou, I., Liu, D., Lee, D. T. & Chang, S. (2012). Robust visual domain adaptation with low-rank reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"757_CR31","doi-asserted-by":"crossref","unstructured":"Lazebnik, S., Schmid, C., & Ponce, J. (2006). Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE Conference on Computer Vision and PatternRecognition (CVPR)..","DOI":"10.1109\/CVPR.2006.68"},{"key":"757_CR32","doi-asserted-by":"crossref","unstructured":"Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning.","DOI":"10.1145\/1553374.1553453"},{"key":"757_CR33","unstructured":"Liu, C., Zhu, S.-C., & Shum, H.-Y. (2001). Learning inhomogeneous gibbs model of faces by minimax entropy. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 281\u2013287."},{"key":"757_CR34","unstructured":"Lounici, K., Tsybakov, A. B., Pontil, M., & van de Geer, S. A. (2009). Taking advantage of sparsity in multi-task learning. In: Proceedings of the 22nd Conference on Learning Theory."},{"key":"757_CR35","doi-asserted-by":"crossref","unstructured":"Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60, 91\u2013110.","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"757_CR36","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1109\/78.258082","volume":"41","author":"S Mallat","year":"1993","unstructured":"Mallat, S., & Zhang, Z. (1993). Matching pursuit in a time-frequency dictionary. IEEE Transactions on Signal Processing, 41, 3397\u20133415.","journal-title":"IEEE Transactions on Signal Processing"},{"key":"757_CR37","doi-asserted-by":"crossref","unstructured":"Marszalek, M., & Schmid, C. (2007). Accurate object localization with shape masks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2007.383085"},{"key":"757_CR38","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.acha.2012.03.006","volume":"34","author":"S Nama","year":"2013","unstructured":"Nama, S., Daviesb, M. E., Eladc, M., & Gribonval, R. (2013). The cosparse analysis model and algorithms. Applied and Computational Harmonic Analysis, 34, 30\u201356.","journal-title":"Applied and Computational Harmonic Analysis"},{"key":"757_CR39","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1023\/A:1008923215028","volume":"11","author":"R Neal","year":"2001","unstructured":"Neal, R. (2001). Annealed importance sampling. Statistics and Computing, 11, 125\u2013139.","journal-title":"Statistics and Computing"},{"key":"757_CR40","doi-asserted-by":"crossref","unstructured":"Neal, R. (2011). MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo.","DOI":"10.1201\/b10905-6"},{"key":"757_CR41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1214\/09-AOS776","volume":"39","author":"G Obozinski","year":"2011","unstructured":"Obozinski, G., Wainwright, M. J., & Jordan, M. I. (2011). Support union recovery in high-dimensional multivariate regression. Annals of Statistics, 39, 1\u201347.","journal-title":"Annals of Statistics"},{"key":"757_CR42","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1038\/381607a0","volume":"381","author":"BA Olshausen","year":"1996","unstructured":"Olshausen, B. A., & Field, D. J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381, 607\u2013609.","journal-title":"Nature"},{"key":"757_CR43","doi-asserted-by":"crossref","unstructured":"Pati, Y. C., Rezaiifar, R., & Krishnaprasad, P. S. (1993). Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In: Proceedings of the 27th Asilomar Conference on Signals, Systems and Computers, pp. 40\u201344.","DOI":"10.1109\/ACSSC.1993.342465"},{"key":"757_CR44","doi-asserted-by":"crossref","unstructured":"Pietra, S. D., Pietra, V. D., & Lafferty, J. (1997). Inducing features of random fields. IEEE Transactions on PAMI, 19, 380\u2013393.","DOI":"10.1109\/34.588021"},{"key":"757_CR45","doi-asserted-by":"crossref","unstructured":"Ranzato, M., & Hinton, G. E. (2010). Modeling pixel means and covariances using factorized third-order Boltzmann machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2010.5539962"},{"key":"757_CR46","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1038\/14819","volume":"2","author":"M Riesenhuber","year":"1999","unstructured":"Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2, 1019\u20131025.","journal-title":"Nature Neuroscience"},{"key":"757_CR47","doi-asserted-by":"crossref","unstructured":"Roth, S., & Black, M. (2009). Fields of experts. International Journal of Computer Vision, 82, 205\u2013229.","DOI":"10.1007\/s11263-008-0197-6"},{"key":"757_CR48","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1109\/TSP.2009.2036477","volume":"58","author":"R Rubinstein","year":"2010","unstructured":"Rubinstein, R., Zibulevsky, M., & Elad, M. (2010). Double sparsity: Learning sparse dictionaries for sparse signal approximation. IEEE Transactions on Signal Processing, 58, 1553\u20131564.","journal-title":"IEEE Transactions on Signal Processing"},{"key":"757_CR49","doi-asserted-by":"crossref","unstructured":"Saenko, K., Kulis, B., Fritz, M. & Darrell, T. (2010). Adapting visual category models to new domains. In: Proceedings of the European Conference on Computer Vision (ECCV).","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"757_CR50","doi-asserted-by":"crossref","unstructured":"Shekhar, S., Patel, V. M., Nguyen, H. V., & Chellappa, R. (2013). Generalized domain adaptive dictionaries. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2013.53"},{"key":"757_CR51","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1109\/TPAMI.2011.227","volume":"34","author":"Z Si","year":"2012","unstructured":"Si, Z., & Zhu, S. C. (2012). Learning hybrid image template (HIT) by information projection. IEEE Transactions on PAMI, 34, 1354\u20131367.","journal-title":"IEEE Transactions on PAMI"},{"key":"757_CR52","unstructured":"Smolensky, P. (1986). Information processing in dynamical systems: Foundations of harmony theory. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing (pp. 194\u2013281). Cambridge: MIT Press."},{"key":"757_CR53","unstructured":"Teh, Y. W., Welling, M., Osindero, S., & Hinton, G. E. (2003). Energy-based models for sparse overcomplete representations. Journal of Machine Learning Research, 4, 1235\u20131260."},{"key":"757_CR54","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, B, 58, 267\u2013288.","journal-title":"Journal of the Royal Statistical Society, B"},{"key":"757_CR55","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.sigpro.2005.05.030","volume":"86","author":"J Tropp","year":"2006","unstructured":"Tropp, J., Gilbert, A., & Straus, M. (2006). Algorithms for simultaneous sparse approximation. part I: Greedy pursuit. Journal of Signal Processing, 86, 572\u2013588.","journal-title":"Journal of Signal Processing"},{"key":"757_CR56","doi-asserted-by":"crossref","unstructured":"Tuytelaars, T., Lampert, C. H., Blaschko, M. B., & Buntine, W. (2009). Unsupervised object discovery: A comparison. International Journal of Computer Vision, 88(2), 284-302.","DOI":"10.1007\/s11263-009-0271-8"},{"key":"757_CR57","doi-asserted-by":"crossref","unstructured":"Vapnik, V. N. (2000). The nature of statistical learning theory. Berlin: Springer.","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"757_CR58","unstructured":"Welling, M., Hinton, G. E., & Osindero, S. (2003). Learning sparse topographic representations with products of student-t distributions. In: Proceedings of Advances in Neural Information Processing Systems (NIPS)."},{"key":"757_CR59","doi-asserted-by":"crossref","unstructured":"Wu, Y. N., Si, Z., Gong, H., & Zhu, S. C. (2010). Learning active basis model for object detection and recognition. International Journal of Computer Vision, 90, 198\u2013235.","DOI":"10.1007\/s11263-009-0287-0"},{"key":"757_CR60","doi-asserted-by":"crossref","unstructured":"Xie, J., Hu, W., Zhu, S. C., & Wu, Y. N. (2014). Learning Inhomogeneous FRAME models for object patterns. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2014.136"},{"key":"757_CR61","doi-asserted-by":"crossref","unstructured":"Yang, M., Zhang, L., Feng, X., & Zhang, D. (2011). Fisher discrimination dictionary learning for sparse representation. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 543-550.","DOI":"10.1109\/ICCV.2011.6126286"},{"key":"757_CR62","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1080\/17442509908834179","volume":"65","author":"L Younes","year":"1999","unstructured":"Younes, L. (1999). On the convergence of Markovian stochastic algorithms with rapidly decreasing ergodicity rates. Stochastics and Stochastic Reports, 65, 177\u2013228.","journal-title":"Stochastics and Stochastic Reports"},{"key":"757_CR63","doi-asserted-by":"crossref","unstructured":"Zeiler, M., Taylor, G., & Fergus, R. (2011). Adaptive deconvolutional networks for mid and high level feature learning. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV).","DOI":"10.1109\/ICCV.2011.6126474"},{"key":"757_CR64","doi-asserted-by":"crossref","unstructured":"Zhu, L., Lin, C., Huang, H., Chen, Y., & Yuille, A. (2008). Unsupervised structure learning: hierarchical recursive composition, suspicious coincidence and competitive exclusion. In: Proceedings of the European Conference on Computer Vision (ECCV).","DOI":"10.1007\/978-3-540-88688-4_56"},{"key":"757_CR65","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1561\/0600000018","volume":"2","author":"SC Zhu","year":"2006","unstructured":"Zhu, S. C., & Mumford, D. B. (2006). A stochastic grammar of images. Foundations and Trends in Computer Graphics and Vision, 2, 259\u2013362.","journal-title":"Foundations and Trends in Computer Graphics and Vision"},{"key":"757_CR66","first-page":"1627","volume":"9","author":"SC Zhu","year":"1998","unstructured":"Zhu, S. C., Wu, Y. N., & Mumford, D. B. (1998). Minimax entropy principle and its application to texture modeling. Neural Computation, 9, 1627\u20131660.","journal-title":"Neural Computation"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-014-0757-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11263-014-0757-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-014-0757-x","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T09:09:07Z","timestamp":1717405747000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11263-014-0757-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,10,2]]},"references-count":66,"journal-issue":{"issue":"2-3","published-print":{"date-parts":[[2015,9]]}},"alternative-id":["757"],"URL":"https:\/\/doi.org\/10.1007\/s11263-014-0757-x","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,10,2]]}}}