Adaptive unsupervised multi-view feature selection for visual concept recognition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7724 LNCS, Issue PART 1, 2013, Pages 343-357

  Feng, Yinfu ^a   Xiao, Jun ^a   Zhuang, Yueting ^a   Liu, Xiaoming ^b  

^a ZHEJIANG UNIVERSITY   (China)

^b Michigan State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONCEPT RECOGNITION; DISCRIMINATIVE FEATURES; FEATURE SELECTION METHODS; HUMAN-ACTION RECOGNITION; MULTI-VIEW LEARNING; UNSUPERVISED FEATURE SELECTION; UNSUPERVISED LEARNING METHOD; VISUAL CONCEPT RECOGNITION;

BENCHMARKING; GESTURE RECOGNITION; ITERATIVE METHODS; LEARNING ALGORITHMS; OBJECT RECOGNITION; REGRESSION ANALYSIS;

COMPUTER VISION;

EID: 84875889840     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-37331-2_26     Document Type: Conference Paper

References (30)

1. Lisin, D., Mattar, M., Blaschko, M., Learned-Miller, E., Benfield, M.: Combining local and global image features for object class recognition. In: IEEE Workshop on Learning in CVPR (2005)
2. Sun, X., Chen, M., Hauptmann, A.: Action recognition via local descriptors and holistic features. In: CVPR Workshops, pp. 58-65. IEEE (2009)
3. Cao, L., Tian, Y., Liu, Z., Yao, B., Zhang, Z., Huang, T.S.: Action detection using multiple spatial-temporal interest point features. In: ICME, pp. 340-345 (2010)
4. Lowe, D.: Distinctive image features from scale-invariant keypoints. J. of CV 60, 91-110 (2004)
5. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. J. of TPAMI 28, 2037-2041 (2006)
6. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. J. of TPAMI 27, 1615-1630 (2005)
7. Xia, T., Tao, D., Mei, T., Zhang, Y.: Multiview spectral embedding 40, 1438-1446 (2010)
8. Bickel, S., Scheffer, T.: Multi-view clustering. In: ICDM, pp. 19-26 (2004)
9. Okun, O., Priisalu, H.: Multiple views in ensembles of nearest neighbor classifiers. In: ICML Workshop on Learning with Multiple Views (2005)
10. Yang, Y., Shen, H., Ma, Z., Huang, Z., Zhou, X.: L21-norm regularized discriminative feature selection for unsupervised learning. In: IJCAI (2011)
11. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley-Interscience, Chichester (2001)
12. Zhao, Z., Wang, L., Liu, H.: Efficient spectral feature selection with minimum redundancy. In: AAAI (2010)
13. He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: NIPS (2005)
14. Zhao, Z., Liu, H.: Spectral feature selection for supervised and unsupervised learning. In: ICML, pp. 1151-1157 (2007)
15. Cai, D., Zhang, C., He, X.: Unsupervised feature selection for multi-cluster data. In: KDD, pp. 333-342 (2010)
16. Zechao, L., Yi, Y., Jing, L., Xiaofang, Z., Hanqing, L.: Unsupervised feature selection using nonegative spectral analysis. In: AAAI (2012)
17. Shi, J., Malik, J.: Normalized cuts and image segmentation. J. of TPAMI 22, 888-905 (2000)
18. Piepel, G.: Robust regression and outlier detection. Technometrics 31, 260-261 (1989)
19. 21-norm. In: CIKM, pp. 673-682 (2011)
20. 2,1-norms minimization. NIPS 23, 1813-1821 (2010)
21. Wang, M., Hua, X.S., Yuan, X., Song, Y., Dai, L.R.: Optimizing multi-graph learning: Towards a unified video annotation scheme. In: ACM MM, pp. 862-870 (2007)
22. Yang, Y., Shen, H.T., Nie, F., Ji, R., Zhou, X.: Nonnegative spectral clustering with discriminative regularization. In: AAAI (2011)
23. Boyd, S., Vandenberghe, L.: Convex optimization. Cambridge Univ. Pr. (2004)
24. Duygulu, P., Barnard, K., de Freitas, J.F.G., Forsyth, D.: Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 97-112. Springer, Heidelberg (2002)
25. Chua, T.S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.T.: Nus-wide: A real-world web image database from national university of singapore. In: CIVR (2009)
26. Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: ICCV, pp. 1395-1402 (2005)
27. Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local SVM approach. In: ICPR, vol. 3, pp. 32-36. IEEE (2004)
28. Laptev, I.: On space-time interest points. J. of IJCV 64, 107-123 (2005)
29. Chen, M., Hauptmann, A.: Mosift: Recognizing human actions in surveillance videos. In: CMU-CS-09-161 (2009)
30. Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. J. of ACM TIST 2, 1-27 (2011)