Manifold regularized cross-modal embedding for zero-shot learning

Information Sciences

Volumn 378, Issue , 2017, Pages 48-58

  Ji, Zhong ^a   Yu, Yunlong ^a   Pang, Yanwei ^a   Guo, Jichang ^a   Zhang, Zhongfei ^b  

^a TIANJIN UNIVERSITY   (China)

^b State University of New York   (United States)

Author keywords

Cross modal embedding; Domain adaptation; Image classification; Manifold; Zero shot learning

Indexed keywords

IMAGE CLASSIFICATION; KNOWLEDGE MANAGEMENT;

CLOSED FORM SOLUTIONS; CROSS-MODAL; DOMAIN ADAPTATION; INTRINSIC STRUCTURES; MANIFOLD; SEMANTIC CONSISTENCY; SEMANTIC INFORMATION; ZERO-SHOT LEARNING;

SEMANTICS;

EID: 84994341314     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2016.10.025     Document Type: Article

References (53)

1. [1] Alex, K., Sutskever, I., Hinton, G.E., ImageNet classification with deep convolutional neural networks. Advance in Neural Information Processing Systems, 2012, 1097–1105.
2. [2] Andriy, M., Hinton, G.E., Three new graphical models for statistical language modelling. International Conference on Machine Learning, 2007, 641–648.
3. [3] Akata, Z., Reed, S., Walter, D., Lee, H., Schiele, B., Evaluation of output embeddings for fine-grained image classification. IEEE Conference on Computer Vision and Pattern Recognition, 2015, 2927–2936.
4. [4] Akata, Z., Malinowski, M., Fritz, M., Schiele, B., et al. Multi-cue zero-shot learning with strong supervision. IEEE Conference on Computer Vision and Pattern Recognition, 2016, 1921–1931.
5. [5] Al-Halah, Z., Tapaswi, M., Stiefelhagen, R., Recovering the missing link: Predicting class-attribute associations for unsupervised zero-shot learning. IEEE Conference on Computer Vision and Pattern Recognition, 2016, 856–866.
6. [6] Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C., A neural probabilistic language model. J. Mach. Learn. Res. 3 (2003), 1137–1155.
7. [7] Deng, J., Ding, N., Jia, Y.Q., et al. Large-scale object classification using label relation graphs. European Conference on Computer Vision, 2014, 48–64.
8. [8] Frome, A., Corrado, G.S., Shlens, J., Bengio, S., et al. Devise: A deep visual-semantic embedding model. Advance in Neural Information Processing Systems, 2013, 2121–2129.
9. [9] Fu, Y.W., Hospedales, T.M., Xiang, T., Gong, S.G., Transductive multi-view zero-shot learning. IEEE Trans. Pattern Anal. Mach. Intell. 37:11 (2015), 2332–2345.
10. [10] Fu, Z., Wu, X., Guan, C., et al. Towards efficient multi-keyword fuzzy search over encrypted outsourced data with accuracy improvement. IEEE Trans. Inf. Forensic. Secur., 2016, 10.1109/TIFS.2016.2596138.
11. [11] Fu, Z.Y., Xiang, T., Kodirov, E, Gong, S.G., Zero-shot object recognition by semantic manifold distance. IEEE Conference on Computer Vision and Pattern Recognition, 2015, 2635–2644.
12. [12] Gu, B., Sun, X., Sheng, V.S., Structural minimax probability machine. IEEE Trans. Neural Netw. Learn, 2016, 10.1109/TNNLS.2016.2544779.
13. [13] Gu, B., Sheng, V.S., Tay, K.Y., et al. Incremental support vector learning for ordinal regression. IEEE Trans. Neural Netw. Learn. 26:7 (2015), 1403–1416.
14. [14] Gu, B., Sheng, V.S., Wang, Z., et al. Incremental learning for ν;-support vector regression. Neural Netw. 67 (2015), 140–150.
15. [15] Gao, J., Huang, R., Li, H., Sub-domain adaptation learning methodology. Inform. Sci. 288 (2015), 237–256.
16. [16] Guo, K., Zhang, R., Zhou, Z., Combined retrieval: A convenient and precise approach for Internet image retrieval. Inform. Sci 358 (2016), 151–163.
17. [17] Hong, C.Q., Yu, J., You, J., et al. Multi-view ensemble manifold regularization for 3D object recognition. Inform. Sci. 320 (2015), 395–405.
18. [18] He, X.F., Yan, S.C., Hu, Y.X., Niyogi, P., Zhang, H.J., Face recognition using Laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27:3 (2005), 328–340.
19. [19] He, K.M., Zhang, X.Y., Ren, S.Q., Sun, J, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of International Conference on Computer Vision, 2015, 1026–1034.
20. [20] Jayaraman, D., Grauman, K., Zero-shot recognition with unreliable attributes. Adv. Neural Inf. Process. Syst., 2014, 3464–3472.
21. [21] Ji, Z., Pang, Y.W., He, Y.Q., Zhang, H.F., Semi-supervised LPP algorithms for learning-to-rank-based visual search reranking. Inf. Sci. 302 (2015), 83–93.
22. [22] Ji, Z., Pang, Y., Li, X., Relevance preserving projection and ranking for web image search reranking. IEEE Trans. Image Process. 24:11 (2015), 4137–4147.
23. [23] Z. Ji, Y.Z. Xie, Y.W. Pang, et al., Zero-shot learning with multi-battery factor analysis, arXiv: 1606.09349, 2016.
24. [24] Lazaridou, A., Bruni, E., Baroni, M., Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world. Proc. ACL 1 (2014), 1403–1414.
25. [25] Li, J, Li, X., Yang, B., et al. Segmentation-based image copy-move forgery detection scheme. IEEE Trans. Inf. Forensic. Secur 10:3 (2015), 507–518.
26. [26] Liu, L., Shao, L., Li, X.L., Evolutionary compact embedding for large-scale image Classification. Inf. Sci. 316 (2015), 567–581.
27. [27] Lampert, C.H., Nickisch, H., Harmeling, S., Attribute-based classification for zero-shot visual object categorization. IEEE Trans. Pattern Anal. Mach. Intell. 36:3 (2014), 453–465.
28. [28] Liu, M.X., Zhang, D.Q., Chen, S.C., Attribute relation learning for zero-shot classification. Neurocomputing 139 (2014), 34–46.
29. [29] Lin, W.C., Tsai, C.F., Chen, Z.Y., et al. Keypoint selection for efficient bag-of-words feature generation and effective image classification. Inform. Sci. 329 (2016), 33–51.
30. [30] Li, X., Guo, Y.H., Max-margin zero-shot learning for multi-class classification. International Conference on Artificial Intelligence and Statistics, 2015, 626–634.
31. [31] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J., Distributed representations of words and phrases and their compositionality. Advance in Neural Information Processing Systems, 2013, 3111–3119.
32. [32] Norouzi, M., Mikolov, T., Bengio, S., et al. Zero-shot learning by convex combination of semantic embeddings. Proceedings of International Conference on Learning Representations, 2014, 1–9.
33. [33] Pennington, J., Socher, R., Manning, C.D., Glove: Global vectors for word representation. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2014, 1532–1543.
34. [34] Pan, Y.W., Yao, T., Mei, T., et al. Click-through-based cross-view learning for image search. Proceedings of International ACM SIGIR Conference on Research & Development in Information Retrieval, 2014, 717–726.
35. [35] Pang, Y., Zhu, H., Li, X., Pan, J., Motion blur detection with an indicator function for surveillance robots. IEEE Trans. Ind. Electr 63:9 (2016), 5592–5601.
36. [36] Romera-Paredes, B., Torr, P.H., An embarrassingly simple approach to zero-shot learning. International Conference on Machine Learning, 2015, 1–10.
37. [37] Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition. Proceedings of International Conference on Learning Representations, 2015, 1–13.
38. [38] Socher, R., Ganjoo, M., Manning, C.D., Ng, A., Zero-shot learning through cross-modal transfer. Advances in Neural Information Processing Systems, 2013, 935–943.
39. [39] Wah, C., Branson, S., Perona, P., Belongie, S., Multiclass recognition and part localization with humans in the loop. Proceedings of International Conference on Computer Vision, 2011, 2524–2531.
40. [40] Wang, D., Gao, X., Wang, X., Multimodal discriminative binary embedding for large-scale cross-modal retrieval. IEEE Trans. Image Process. 25:10 (2016), 4540–4554.
41. [41] Wang, K., Tang, J., Wang, N., Semantic Boosting Cross-Modal Hashing for efficient multimedia retrieval. Inf. Sci. 330 (2016), 199–210.
42. [42] Wang, W.R., Arora, R., Livescu, K., Bilmes, J., On deep multi-view representation learning. International Conference on Machine Learning, 2015, 1083–1092.
43. [43] Wen, X., Shao, L., Xue, Y., Fang, W., A rapid learning algorithm for vehicle classification. Inf. Sci. 295 (2015), 395–406.
44. [44] Xu, C., Tao, D.C., Xu, C., A survey on multi-view learning. Comput. Res. Repository, 2013, 1–59.
45. [45] Xu, C., Tao, D.C., Xu, C., Large-margin multi-view information bottleneck. IEEE Trans. Pattern Anal. Mach. Intell. 36:8 (2014), 1559–1572.
46. [46] Xu, C., Tao, D.C., Xu, C., Multi-view intact space learning. IEEE Trans. Pattern Anal. Mach. Intell. 37:12 (2015), 2531–2544.
47. [47] Xian, Y.Q., Akata, Z., Sharma, G., et al. Latent embeddings for zero-shot classification. IEEE Conference on Computer Vision and Pattern Recognition, 2016, 1–14.
48. [48] Xia, Z., Wang, X., Zhang, L., Qin, Z., et al. A privacy preserving and copy-deterrence content-based image retrieval scheme in cloud computing, IEEE Trans. Inf. Forensic. Secur., 2016, 10.1109/TIFS.2016.2590944.
49. [49] Yu, J., Rui, Y., Tao, D.C., Click prediction for web image reranking using multimodal sparse coding. IEEE Trans. Image Process 23:5 (2014), 2019–2032.
50. [50] Yu, J., Rui, Y., Tang, Y.Y., Tao, D.C., et al. High-order distance-based multi-view stochastic learning in image classification. IEEE Trans. Cyber 44:12 (2014), 2431–2442.
51. [51] Yu, J., Tao, D., Li, J., Cheng, J., et al. Semantic preserving distance metric learning and applications. Inf. Sci. 281 (2014), 674–686.
52. [52] Yan, S.C., Xu, D., Zhang, B.Y., Zhang, H.J., et al. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29:1 (2007), 40–51.
53. [53] Zhou, Z., Wang, Y., Wu, Q., et al. Effective and efficient global context verification for image copy detection. IEEE Trans. Inf. Forensic. Secur., 2016, 10.1109/TIFS.2016.2601065.