Semantic embedding for indoor scene recognition by weighted hypergraph learning

Signal Processing

Volumn 112, Issue , 2015, Pages 129-136

  Yu, Jun ^a   Hong, Chaoqun ^b   Tao, Dapeng ^c,d   Wang, Meng ^e  

^a HANGZHOU DIANZI UNIVERSITY   (China)

^b XIAMEN UNIVERSITY OF TECHNOLOGY   (China)

^c SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

^d CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^e HEFEI UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Attributes learning; Indoor scenes classification; Semantic information; Weighted hypergraph

Indexed keywords

SEMANTICS;

ATTRIBUTES LEARNING; CONVENTIONAL METHODS; HYPERGRAPH; SCENE RECOGNITION; SEMANTIC EMBEDDING; SEMANTIC GAP; SEMANTIC INFORMATION; VISUAL FEATURE;

CLASSIFICATION (OF INFORMATION);

EID: 85027934796     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2014.07.027     Document Type: Article

References (41)

1. A. Oliva, and A. Torralba Modeling the shape of the scene: a holistic representation of the spatial envelope Int. J. Comput. Vis. 42 2001 145 175
2. A. Quattoni, A. Torralba, Recognizing indoor scenes, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 413-420.
3. J. Yu, D. Tao, Y. Rui, and J. Cheng Pairwise constraints based multiview features fusion for scene classification Pattern Recognit. 46 2 2013 483 496
4. L. Li, H. Su, E. Xing, F. Li, Object bank: a high-level image representation for scene classification and semantic feature sparsification, in: Proceedings of the Neural Information Processing Systems (NIPS), 2010.
5. C. Harris, M. Stephens, A combined corner and edge detector, in: Proceedings of Alvey Vision Conference, 1988, pp. 147-151.
6. D.G. Lowe Distinctive image features from scale-invariant keypoints Int. J. Comput. Vis. 60 2 2004 91 110
7. K. Mikolajczyk, and C. Schmid Scale & affine invariant interest point detectors Int. J. Comput. Vis. 60 1 2004 63 86
8. Y. Su, and F. Jurie Improving image classification using semantic attributes Int. J. Comput. Vis. 100 1 2012 59 77
9. A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth Describing objects by their attributes Comput. Vis. Pattern Recognit. 2009 1778 1785
10. J. Vogel, and B. Schiele Semantic modeling of natural scenes for content-based image retrieval Int. J. Comput. Vis. 72 2 2007 133 157
11. L. Torresani, M. Szummer, A. Fitzgibbon, Efficient object category recognition using classemes, in: Proceedings of ECCV, 2010.
12. A. Ude, and R. Dillmann Vision-based robot path planning Adv. Robot Kinemat. Comput. Geom. 1994 505 512
13. N. Dimitrova, H.-J. Zhang, I. Sezan, T.S. Huang, and A. Zakhor Applications of video-content analysis and retrieval IEEE Multimedia 9 3 2002 42 55
14. D. Tao, X. Tang, X. Li, and X. Wu Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval IEEE Trans. Pattern Anal. Mach. Intell. 28 7 2006 1088 1099
15. O. Chapelle, A. Zien, and B. Scholkopf Semi- Supervised Learning 2006 MIT Press Cambridge, MA
16. X. Zhu, Semi-supervised learning literature survey, Technical Report, University of Wisconsin-Madison, Madison, 2005.
17. R. He, W. Zheng, Nonnegative sparse coding for discriminative semi-supervised learning, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, 2011, pp. 2849-2856.
18. P. Mallapragada, R. Jin, A. Jain, and Y. Liu SemiBoost: boosting for semi-supervised learning IEEE Trans. Pattern Anal. Mach. Intell. 31 11 2009 2000 2014
19. J. Yu, D. Tao, and M. Wang Adaptive hypergraph learning and its application in image classification IEEE Trans. Image Process. 21 7 2012 3262 3272
20. D. Zhou, J. Huang, B. Schölkopf, Learning with hypergraphs: clustering, classification, and embedding, in: Proceedings of Neural Information Processing Systems, Vancouver, BC, Canada, 2006, pp. 1601-1608.
21. Y. Huang, Q. Liu, F. Lv, Y. Gong, and D. Metaxas Unsupervised image categorization by hypergraph partition IEEE Trans. Pattern Anal. Mach. Intell. 33 6 2011 1266 1273
22. Z. Zhao, H. Liu, Semi-supervised feature selection via spectral analysis, in: Proceedings of SDM, 2007, pp. 641-646.
23. J. Ren, Z. Qiu, W. Fan, H. Cheng, P. Yu, Forward semi-supervised feature selection, in: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD '08), 2008, pp. 970-976.
24. H. Hotelling Analysis of a complex of statistical variables into principal components J. Educ. Psychol. 24 1933 417 441
25. V. Ferrari, A. Zisserman, Learning visual attributes, in: Proceedings of NIPS, 2007.
26. J. Vogel, and B. Schiele A semantic typicality measure for natural scene categorization Pattern Recognit. 3175 2004 195 203
27. L. Li, H. Su, E. Xing, F. Li, Object bank: a high-level image representation for scene classification & semantic feature sparsification, in: Proceedings of NIPS, 2010, pp. 1378-1386.
28. L. Li, H. Su, Y. Lim, and F. Li Objects as attributes for scene classification Trends Top. Comput. Vis. Lect. Notes Comput. Sci. 6553 2012 57 69
29. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, Y. Gong, Locality-constrained linear coding for image classification, in: Proceedings of CVPR, 2010.
30. M. Pandey, S. Lazebnik, Scene recognition and weakly supervised object localization with deformable part-based models, in: Proceedings of ICCV, 2011, pp. 1307-1314.
31. J. Wu, and J.M. Rehg CENTRIST: a visual descriptor for scene categorization Pattern Anal. Mach. Intell. 3 2010 5
32. Y. Gao, M. Wang, Z. Zha, J. Shen, X. Li, and X. Wu Visual-textual joint relevance learning for tag-based social image search IEEE Trans. Image Process. 22 1 2013 363 376
33. Y. Gao, M. Wang, D. Tao, R. Ji, and Q. Dai 3-D object retrieval and recognition with hypergraph analysis IEEE Trans. Image Process. 21 9 2012 4290 4303
34. Y. Gao, M. Wang, H. Luan, J. Shen, S. Yan, and D. Tao Tag-based social image search with visual-text joint hypergraph learning ACM Multimedia 2011 1517 1520
35. S. Ramalingam, J. Pillai, A. Jain, and Y. Taguchi Manhattan junction catalogue for spatial reasoning of indoor scenes Comput. Vis. Pattern Recognit. 2013 3065 3072
36. L. Del Pero, J. Bowdish, D. Fried, B. Kermgard, E. Hartley, and K. Barnard Bayesian geometric modeling of indoor scenes Comput. Vis. Pattern Recognit. 2012 2719 2726
37. P. Espinace, T. Kollar, A. Soto, N. Roy, Indoor scene recognition through object detection, in: Proceedings of International Conference on Robotics and Automation Anchorage Convention District, 2010, pp. 1406-1413.
38. MIT Indoor Scene Database: http://web.mit.edu/torralba/www/indoor.html.
39. L. Zhang, Y. Gao, C. Hong, Y. Feng, J. Zhu, and D. Cai Feature correlation hypergraph: exploiting high-order potentials for multimodal recognition IEEE Trans. Cybernet. 2013 10.1109/TCYB.2013.2285219
40. R. Ji, Y. Gao, R. Hong, Q. Liu, D. Tao, and X. Li Spectral-spatial constraint hyperspectral image classification IEEE Trans. Geosci. Remote Sens. 52 3 2014 1811 1824
41. Y. Gao, M. Wang, Z. Zha, Q. Tian, Q. Dai, and N. Zhang Less is more: efficient 3-D object retrieval with query view selection IEEE Trans. Multimedia 13 5 2011 1007 1018