The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.02 - April-June (2012 vol.19)
pp: 58-68
Chunjie Zhang , Nat. Lab. of Pattern Recognition, China
ABSTRACT
Using higher-level visual elements to represent images, the authors have developed a sparsity-constrained bilinear model (SBLM) and have combined a set of SBLMs in a boosting-like procedure to enhance performance.
INDEX TERMS
object recognition, image representation, SBLM, sparsity-constrained bilinear model, object recognition, higher-level visual elements, image representation, boosting-like procedure, Visualization, Image processing, Object recognition, Image representation, Robustness, Adaptation model, Video communication, Information retrieval, Computer vision, image/video retrieval, multimedia, computer vision, object recognition, image processing
CITATION
Chunjie Zhang, "A Boosting, Sparsity- Constrained Bilinear Model for Object Recognition", IEEE MultiMedia, vol.19, no. 2, pp. 58-68, April-June 2012, doi:10.1109/MMUL.2011.20
REFERENCES
1. A. Berg, T. Berg, and J. Malik, "Shape Matching and Object Recognition Using Low Distortion Correspondences," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 05), IEEE CS Press, vol. 1, 2005, pp. 26–33.
2. K. Grauman and T. Darrell, "The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features," Proc. 10th Int'l Conf. Computer Vision (ICCV 05), IEEE CS Press, 2005, pp. 1458–1465.
3. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 06), IEEE CS Press, 2006, pp. 2169–2178.
4. L. Fei-Fei, R. Fergus, and P. Perona, "Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 04) Workshop on Generative Model Based Vision, IEEE CS Press, 2004, pp 178–186.
5. G. Wang, Y. Zhang, and L. Fei-Fei, "Using Dependent Regions for Object Categorization in a Generative Framework," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 06), IEEE CS Press, 2006, pp. 1597–1604.
6. A. Bosch, A. Zisserman, and X. Munoz, "Image Classification Using Random Forests and Ferns," Proc. IEEE 11th Int'l Conf. Computer Vision (ICCV 07), IEEE CS Press, 2007, pp. 1–8.
7. H. Zhang et al., "SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 06), IEEE CS Press, 2006, pp. 2126–2136.
8. A. Bosch, A. Zisserman, and X. Munoz, "Representing Shape with a Spatial Pyramid Kernel," Proc. 6th ACM Int'l Conf. Image and Video Retrieval (CIVR 07), ACM Press, 2007, pp. 401–408.
9. M. Varma and D. Ray, "Learning the Discriminative Power-Invariance Trade-Off," Proc. IEEE 11th Int'l Conf. Computer Vision (ICCV 07), IEEE CS Press, 2007, pp. 1–8.
10. F. Moosmann, B. Triggs, and F. Jurie, "Fast Discriminative Visual Codebooks Using Randomized Clustering Forests," Proc. 20th Ann. Conf. Neural Information Processing Systems (NIPS 06), Advances in Neural Information Processing Systems 19, MIT Press, 2006, pp. 985–992.
11. L. Yang et al., "Unifying Discriminative Visual Codebook Generation with Classifier Training for Object Category Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 08), IEEE CS Press, 2008, pp. 1–8.
12. O. Boiman, E. Shechtman, and M. Irani, "In Defense of Nearest-Neighbor Based Image Classification," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 08), IEEE CS Press, 2008, pp. 1–8.
13. J. Wright et al., "Robust Face Recognition via Sparse Representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 2, 2009, pp. 210–227.
14. J. Friedman, T. Hastie, and R. Tibshirani, "Additive Logistic Regression: A Statistical View of Boosting," The Annals of Statistics, vol. 28, no. 2, 2000, pp. 337–407.
15. H. Lee et al., "Efficient Sparse Coding Algorithms," Advances in Neural Information Processing Systems (NIPS 07), vol. 19, 2007, pp. 801–808.
16. G. Griffin, A. Holub, and P. Perona, "Caltech-256 Object Category Dataset," tech. report, Calif. Inst. of Technology, 2007.
17. J. Gemert et al., "Visual Word Ambiguity," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 7, 2010, pp. 1271–1283.
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool