Internet cross-media retrieval based on deep learning
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Pedronette, 2014, A scalable re-ranking method for content-based image retrieval, Inform. Sciences, 265, 91, 10.1016/j.ins.2013.12.030
Guo, 2015, Content-based image retrieval using error diffusion block truncation coding features, IEEE Trans. Circ. Syst. Video Technol., 25, 466, 10.1109/TCSVT.2014.2358011
Piedra-Fernandez, 2014, Fuzzy content-based image retrieval for oceanic remote sensing, IEEE Trans. Geosci. Rem. Sens., 52, 5422, 10.1109/TGRS.2013.2288732
Li, 2011, Text-based image retrieval using progressive multi-instance learning, Proceedings, 58, 2049
Ahamd, 2003, Old fashion text-based image retrieval using FCA, vol. 2
Lin, 2015, A self-assessment stereo capture model applicable to the internet of things, Sensors, 15, 20925, 10.3390/s150820925
J. Liu, C. Wang, J. Gao, J. Han, Multi-View Clustering via Joint Nonnegative Matrix Factorization, 2013.
Eaton, 2014, Multi-view constrained clustering with an incomplete mapping between views, Knowl. Inf. Syst., 38, 231, 10.1007/s10115-012-0577-7
Hardoon, 2004, Canonical correlation analysis: an overview with application to learning methods, Neural Comput., 16, 2639, 10.1162/0899766042321814
Sun, 2011, Canonical correlation analysis for multilabel classification: a least-squares formulation, extensions, and analysis, IEEE Trans. Softw. Eng., 33, 194
Rasiwasia, 2010, A new approach to cross-modal multimedia retrieval, 251
Chaudhuri, 2009, Multi-view clustering via canonical correlation analysis, 129
Hotelling, 1935, Relations between two sets of variates, Biometrika, 28, 321
Cai, 2013, Multi-view k-means clustering on big data
Sharma, 2012, Generalized multiview analysis: a discriminative latent space, 2160
S. Hao, S. Min, F.F. Li, S. Savarese, Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories, 2009, pp. 213–220.
Zhang, 2013, Transferring training instances for convenient cross-view object classification in surveillance, IEEE Trans. Inf. Foren. Sec., 8, 1632, 10.1109/TIFS.2013.2265089
Ko?o, 2011, A boosting approach to multiview classification with cooperation, 209
Blum, 1998, Combining labeled and unlabeled sata with co-training, 92
Nen, 2011, Multiple kernel learning algorithms, J. Mach. Learn. Res., 12, 2211
Xu, 2010, Simple and efficient multiple kernel learning by group lasso, 1175
Lewis, 2006, Nonstationary kernel combination, 553
Andrew, 2013, Deep canonical correlation analysis, 1247
Gupta, 2010, Nonnegative shared subspace learning and its application to social media retrieval, 1169
Yu, 2007, Multi-output regularized feature projection, IEEE Trans Knowl. Data Eng., 18, 1600
Ando, 2005, A framework for learning predictive structures from multiple tasks and unlabeled data, J. Mach. Learn. Res., 6, 1817
Ji, 2010, A shared-subspace learning framework for multi-label classification, ACM Trans. Knowl. Discov. Data, 4, 890, 10.1145/1754428.1754431
Kong, 2013, Transductive multilabel learning via label set propagation, IEEE Trans Knowl. Data Eng., 99, 704, 10.1109/TKDE.2011.141
Weinberger, 2006, Distance metric learning for large margin nearest neighbor classification, J. Mach. Learn. Res., 10, 207
Qiu, 2003, Color image indexing using btc, IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc., 12, 93
Zhang, 2010, Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor, IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc., 19, 533, 10.1109/TIP.2009.2035882
Guo, 2010, Rotation invariant texture classification using LBP variance (LBPV) with global matching, Pattern Recog., 43, 706, 10.1016/j.patcog.2009.08.017
Wang, 2009, An HOG-LBP human detector with partial occlusion handling, Proceedings, 30, 32
Mohamed, 2012, Acoustic modeling using deep belief networks, IEEE Trans. Audio Speech Lang. Process., 20, 14, 10.1109/TASL.2011.2109382
A.R. Mohamed, G. Dahl, G. Hinton, Deep belief networks for phone recognition 4.
A. Mohamed, G. Hinton, G. Penn, Understanding how deep belief networks perform acoustic modelling, 2012, pp. 4273–4276.
Le, 1989, Representational power of restricted boltzmann machines and deep belief networks, Neural Comput., 20, 1631
Chua, 2009, Nus-wide: a real-world web image database from National University of Singapore, 1
Müller, 2001, Performance evaluation in content-based image retrieval: overview and proposals, Pattern Recog. Lett., 22, 593, 10.1016/S0167-8655(00)00118-5
Blei, 2010, Supervised topic models, Adv. Neural Inf. Process. Syst., 3, 327
Zhuang, 2013, Supervised coupled dictionary learning with group structures for multi-modal retrieval
Wang, 2014, Multi-modal mutual topic reinforce modeling for cross-media retrieval, 307