Efficient Orthogonal Matching Pursuit using sparse random projections for scene and video classification

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 2312-2319

  Vitaladevuni, Shiv N ^a   Natarajan, Pradeep ^a   Prasad, Rohit ^a   Natarajan, Prem ^a  

^a BBN TECHNOLOGIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; HADAMARD; HADAMARD TRANSFORMS; JOHNSON-LINDENSTRAUSS TRANSFORMS; LARGE-SCALE PROBLEM; LOCALITY SENSITIVE HASHING; NOVEL TECHNIQUES; OBJECT CLASSIFICATION; ORTHOGONAL MATCHING PURSUIT; PROJECTION ALGORITHMS; PROJECTION MATRIX; RANDOM PROJECTIONS; SCENE CLASSIFICATION; VIDEO ANALYSIS; VIDEO CATEGORIZATION; VIDEO CLASSIFICATION;

EXPERIMENTS; IMAGE DENOISING; IMAGE PROCESSING;

MATHEMATICAL TRANSFORMATIONS;

EID: 84856672701     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126512     Document Type: Conference Paper

References (34)

1. D. Achlioptas. Database-friendly random projections. In Proc PODS, 2001. 1, 2, 3, 4
2. N. Ailon and B. Chazelle. Approximate nearest neighbors and the fast johnson-lindenstrauss transform. In Proc ACM STOC, 2006. 1, 2, 3, 4, 8
3. N. Ailon and E. Liberty. An almost optimal unrestricted fast johnsonlindenstrauss transform. In Proc SODA, 2011. 3
4. M. Aly, M. Munich, and P. Perona. Indexing in large scale image collections: Scaling properties and benchmark. In ProcWACV, 2011. 2, 3
5. E. V. D. Berg and M. P. Friedlander. Probing the pareto frontier for basis pursuit solutioins. SIAM Jnl. Sci. Comput., 2008. 2
6. E. Bingham and H. Mannila. Random projection in dimensionality reduction: Applications to image and text data. In Proc SIGKDD, 2001. 2, 3
7. Y.-L. Boureau, F. Bach, Y. LeCun, and J. Ponce. Learning mid-level features for recognition. In Proc CVPR, 2010. 1, 4, 6, 7
8. M. S. Charikar. Similarity estimation techniques from rounding algorithms. In Proc STOC, 2002. 2, 3
9. S. F. Cotter, J. Adler, B. D. Rao, and K. Kreutz-Delgado. Forward sequential algorithms for best basis selection. IEE Prof. Vis. Image Signal Process, 146, 1999. 2
10. M. Elad and M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Proc., 2006. 4, 5
11. L. Fei-Fei and P. Perona. A bayesian hierarchical model for learning natural scene categories. In Proc CVPR, 2005. 1, 6, 7
12. A. K. Fletcher and S. Rangan. Orthogonal matching pursuit from noisy measurements: A new analysis. In Proc NIPS, 2009. 2
13. A. C. Gilbert, S. Muthukrishnan, and M. J. Strauss. Approximation of functions over redundant dictionaries using coherence. In Proc. SODA, 2003. 2, 3
14. E. T. Hale, W. Yin, and Y. Zhang. A fixed-point continuation method for '1-regularized minimization with applications to compressed sensing. Technical Report TR07-07, Dept. of Comp. Applied Math, Rice Univ., 2007. 2
15. P. Indyk and R. Motwani. Approximate nearest neighbors: towards removing the curse of dimensionality. In Proc. STOC, 1998. 2, 3
16. B. Kulis and K. Grauman. Kernelized locality-sensitive hashing for scalable image search. In Proc ICCV, 2009. 2, 3
17. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. CVPR, 2006. 7
18. P. Li, T. J. Hastie, and K.W. Church. Very sparse random projections. In Proc SIGKDD, 2006. 1, 2, 3, 4, 8
19. J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online dictionary learning for sparse coding. In Proc ICML, 2009. 2, 6
20. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Trans. PAMI, 2005. 7
21. D. Needell and J. A. Tropp. Cosamp: Iterative signal recovery from incomplete and inaccurate samples. App. Comput. Harmonic Analysis, 2008. 2
22. B. A. Olhausen and D. J. Field. Sparse coding with an overcomplete basis set: A strategy employed by v1? Vision Research, 1997. 1
23. R. Rubinstein, M. Zibulaevsky, and M. Elad. Efficent implementation of the k-svd algorithm using batch orthogonal matching pursuit. Technical Report CS-2008-08, Technion-Israel Institute of Technology, 2008. 2
24. G. Schindler, L. Zitnick, and M. Brown. Internet video category recognition. In Proc. CVPRW '08, 2008. 6
25. Q. Shi, H. Li, and C. Shen. Rapid face recognition using hashing. In Proc CVPR, 2010. 2
26. J. Sivic and A. Zisserman. Video google: A text retrieval approach to object matching in videos. In Proc ICCV, 2003. 6, 7
27. G. Toderici, H. Aradhye, M. Pasca, L. Sbaiz, and J. Yagnik. Finding meaning on youtube: Tag recommendation and category discovery. In Proc CVPR, 2010. 6
28. J. A. Tropp and A. C. Gilbert. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inform. Theory, 2007. 1, 2
29. J. A. Tropp, A. C. Gilbert, S. Muthukrishnan, and M. J. Strauss. Improved sparse approximation over quasi-incoherent dictionaries. In Proc. ICIP, 2003. 2, 3
30. J. Wang, S. Kumar, and S.-F. Chang. Semi-supervised hashing for scalable image retrieval. In Proc CVPR, 2010. 2, 8
31. Y. Wang, J. Yang, W. Yin, and Y. Zhang. A new alternating minimization algorithm for total variation image reconstruction. SIAM Jnl Imaging Sci, 2008. 5, 6, 7
32. Z.Wang, M. Zhao, Y. Song, S. Kumar, and B. Li. Youtubecat: Learning to categorize wild web videos. In Proc. CVPR, 2010. 6, 7
33. J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In Proc CVPR, 2009. 1, 4, 6, 7
34. H. Zhou, T. Hermans, A. Karandikar, and J. M. Rehg. Movie genre classification via scene categorization. In Proc Multimedia, 2010. 6