Adaptive color independent components based SIFT descriptors for image classification

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2010, Pages 2436-2439

  Ai, Danni ^a   Han, Xianhua ^a   Ruan, Xiang ^b   Chen, Yen Wei ^a  

^a RITSUMEIKAN UNIVERSITY   (Japan)

^b OMRON CORPORATION   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR IMAGE REPRESENTATION; COLOR SPACE; DESCRIPTORS; INDEPENDENT COMPONENTS; SIFT DESCRIPTORS;

COLOR IMAGE PROCESSING; FEATURE EXTRACTION; IMAGE ANALYSIS; IMAGE CLASSIFICATION; INDEPENDENT COMPONENT ANALYSIS; MULTIVARIANT ANALYSIS;

COLOR;

EID: 78149488772     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2010.596     Document Type: Conference Paper

References (14)

1. David G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. The International Journal of Computer Vision, 2004.
2. K. E. A. van de Sande, T. Gevers, and C. G. M. Snoek. Evaluation of Color Descriptors for Object and Scene Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008
3. A. Bosch, A. Zisserman, and X. Muoz. Scene classification using a hybrid generative/discriminative approach. IEEE Trans. Pattern Analysis and Machine Intell, 30(04):712-727, 2008
4. A. E. Abdel-Hakim and A. A. Farag. CSIFT: A SIFT Descriptor with Color Invariant Characteristics. Computer Vision and Pattern Recognition (CVPR'06). 2, 1978-1983, 2006
5. X. Y. Zeng, Y. W. Chen, and Z. Nakao. Classification of remotely sensed images using independent component analysis and spatial consistency. Journal of Advanced Computational Intelligence and Intelligent Informatics, 8(2):216-222, 2004
6. G.Buchsbaum and A. Gottschalk. Trichromacy opponent colours coding and optimum colour information transmission in the retina. Proceedings of the Royal Society Lodon, B, 220:89-113, 1983
7. P. Comon, Independent component analysis-a new concept? Signal Processing 36, 287-314, 1994.
8. A.J.Bell and T.J.Sejnowski. An information maximization approach to blind separation and blind deconvolution. Neural Computation, 1129-1159, 1995.
9. J. G. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid. Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study. Beyond Patches workshop, Computer Vision and Pattern Recognition (CVPR), 2006.
10. B. Scholkopf and A. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, Cambridge, MA, 2002.
11. J. Z. Wang, J. Li, and G. Wiederhold. SIMPLIcity: Semantics sensitive Integrated Matching for Picture Libraries. IEEE Trans. On Pattern Analysis and Machine Intelligence, 23(9):947-963, 2001.
12. R. Fergus, P. Perona, and A. Zisserman. A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2005.
13. http://www.robots.ox.ac.uk/~vgg/data.html
14. http://www.vision.caltech.edu/html-files/archive.html