Efficient and effective visual codebook generation using additive kernels

Journal of Machine Learning Research

Volumn 12, Issue , 2011, Pages 3097-3118

  Wu, Jianxin ^a   Tan, Wei Chian ^a   Rehg, James M ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b Georgia Institute of Technology   (United States)

Author keywords

Additive kernel; Histogram intersection kernel; Visual codebook

Indexed keywords

BAG-OF-VISUAL-WORDS; CLUSTER FEATURE; CODE-WORDS; CODEBOOK GENERATION; CODEBOOKS; DATA SETS; DESCRIPTORS; EUCLIDEAN DISTANCE; GAUSSIAN MIXTURE MODEL; HISTOGRAM FEATURES; HISTOGRAM INTERSECTION KERNELS; IMAGE MEASUREMENTS; K-MEANS; K-MEDIAN CLUSTERING; KERNEL K-MEANS; ONE CLASS-SVM; RECOGNITION ACCURACY; SCENE RECOGNITION;

ALGORITHMS; CODES (SYMBOLS); CONTENT BASED RETRIEVAL; FACE RECOGNITION; GRAPHIC METHODS;

STATISTICAL METHODS;

EID: 84855386008     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (41)

1. Ankur Agarwal and Bill Triggs. Multilevel image coding with hyperfeatures. International Journal of Computer Vision, 78(1):15-27, 2008. (Pubitemid 351354422)
2. David Arthur and Sergei Vassilvitskii. k-means++: the advantage of careful seeding. In 18th Symposium on Discrete Algorithms, pages 1027-1035, 2007.
3. Oren Boiman, Eli Shechtman, and Michal Irani. In defense of nearest-neighbor based image classification. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2008.
4. Sabri Boughorbel, Jean-Philippe Tarel, and Nozha Boujemaa. Generalized histogram intersection kernel for image recognition. In Proc. Int'l Conf. on Image Processing, 2005.
5. Chih-Chung Chang and Chih-Jen Lin. LIBSVM: A library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
6. Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, volume 1, pages 886-893, 2005. (Pubitemid 43897286)
7. Janez Demšar. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7:1-30, 2006. (Pubitemid 43022939)
8. Mark Everingham, Andrew Zisserman, Christopher Williams, and Luc Van Gool. The PASCAL visual object classes challenge 2006 (VOC 2006) results, 2006.
9. Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training example: An incremental Bayesian approach tested on 101 object categories. In CVPR 2004, Workshop on Generative-Model Based Vision, 2004.
10. Shenghua Gao, Ivor Wai-Hung Tsang, Liang-Tien Chia, and Peilin Zhao. Local features are not lonely - Laplacian sparse coding for image classification. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2010.
11. Chih-Wei Hsu and Chih-Jen Lin. BSVM, 2006. Software available at http://www.csie.ntu. edu.tw/cjlin/bsvm.
12. Frédéric Jurie and Bill Triggs. Creating efficient codebooks for visual recognition. In The IEEE Conf. on Computer Vision, volume 1, pages 604-610, 2005. (Pubitemid 44055011)
13. Svetlana Lazebnik and Maxim Raginsky. Supervised learning of quantizer codebooks by information loss minimization. IEEE Trans. on Pattern Analysis and Machine Intelligence, 31(7): 1294-1309, 2009.
14. Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, volume II, pages 2169-2178, 2006. (Pubitemid 44931582)
15. Li-Jia Li and Li Fei-Fei. What, where and who? Classifying events by scene and object recognition. In The IEEE Conf. on Computer Vision, 2007.
16. Jingen Liu and Mubarak Shah. Scene modeling using Co-Clustering. In The IEEE Conf. on Computer Vision, 2007.
17. David G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
18. Subhransu Maji and Alexander C. Berg. Max-margin additive classifiers for detection. In The IEEE Conf. on Computer Vision, 2009.
19. Subhransu Maji, Alexander C. Berg, and Jitendra Malik. Classification using intersection kernel support vector machines is efficient. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2008.
20. Frank Moosmann, Eric Nowak, and Frederic Jurie. Randomized clustering forests for image classification. IEEE Trans. on Pattern Analysis and Machine Intelligence, 30(9):1632-1646, 2008.
21. David Nistér and Henrik Stewénius. Scalable recognition with a vocabulary tree. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, volume 2, pages 2161-2168, 2006. (Pubitemid 44931581)
22. Francesca Odone, Annalisa Barla, and Alessandro Verri. Building kernels from binary strings for image matching. IEEE Trans. Image Processing, 14(2):169-180, 2005. (Pubitemid 40191752)
23. Florent Perronnin. Universal and adapted vocabularies for generic visual categorization. IEEE Trans. on Pattern Analysis and Machine Intelligence, 30(7):1243-1256, 2008.
24. James Philbin, Ondřej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman. Lost in quantization: Improving particular object retrieval in large scale image databases. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2008.
25. Ariadna Quattoni and Antonio Torralba. Recognizing indoor scenes. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2009.
26. Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5):1299-1319, 1998. (Pubitemid 128463674)
27. Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alex J. Smola, and Robert C. Williamson. Estimating the support of a high-dimensional distribution. Neural Computation, 13(7):1443-1471, 2001. (Pubitemid 33595028)
28. Josef Sivic and Andrew Zisserman. Video Google: A text retrieval approach to object matching in videos. In The IEEE Conf. on Computer Vision, volume 2, pages 1470-1477, 2003.
29. Michael J. Swain and Dana H. Ballard. Color indexing. International Journal of Computer Vision, 7(1):11-32, 1991.
30. Tinne Tuytelaars and Cordelia Schmid. Vector quantizing feature space with a regular lattice. In The IEEE Conf. on Computer Vision, 2007.
31. Jan C. van Gemert, Jan-Mark Geusebroek, Cor J. Veenman, and Arnold W.M. Smeulders. Kernel codebooks for scene categorization. In European Conf. Computer Vision, 2008.
32. Andrea Vedaldi and Andrew Zisserman. Efficient additive kernels via explicit feature maps. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2010.
33. Julia Vogel and Bernt Schiele. Semantic modeling of natural scenes for content-based image retrieval. International Journal of Computer Vision, 72(2):133-157, 2007. (Pubitemid 46094757)
34. Yair Weiss, Antonio Torralba, and Rob Fergus. Spectral hashing. In Advances in Neural Information Processing Systems 21, pages 1753-1760, 2009.
35. John Winn, Antonio Criminisi, and Thomas Minka. Object categorization by learned universal visual dictionary. In The IEEE Conf. on Computer Vision, volume 2, pages 1800-1807, 2005. (Pubitemid 44042395)
36. JianxinWu. A fast dual method for HIK SVMlearning. In European Conf. Computer Vision, LNCS 6312, pages 552-565, 2010.
37. Jianxin Wu and James M. Rehg. Where am I: Place instance and category recognition using spatial PACT. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 1-8, 2008.
38. Jianxin Wu and James M. Rehg. Beyond the Euclidean distance: Creating effective visual codebooks using the histogram intersection kernel. In The IEEE Conf. on Computer Vision, pages 630-637, 2009.
39. Jianxin Wu and James M. Rehg. CENTRIST: A visual descriptor for scene categorization. IEEE Trans. on Pattern Analysis and Machine Intelligence, 33(8):1489-1501, 2011.
40. Jianchao Yang, Kai Yu, Yihong Gong, and Thomas Huang. Linear spatial pyramid matching using sparse coding for image classification. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2009.
41. Liu Yang, Rong Jin, Rahul Sukthankar, and Frederic Jurie. Unifying discriminative visual codebook generation with classifier training for object category recognition. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2008.