An experimental study of employing visual appearance as a phenotype

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Wolf, Lior ^a   Donner, Yoni ^a  

^a TEL AVIV UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHLORINE COMPOUNDS; COMPUTER VISION; CORRELATION METHODS; FEATURE EXTRACTION; IMAGE PROCESSING; KETONES; PATTERN RECOGNITION; TESTING;

CURRENT TECHNOLOGIES; DATA SETS; EXPERIMENTAL STUDIES; GENETIC SEQUENCING; HIDDEN VARIABLES; LIP MOVEMENTS; SURVEILLANCE VIDEOS; VISUAL APPEARANCES; VISUAL DATA; VISUAL STIMULUS;

SECURITY SYSTEMS;

EID: 51949095989     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587624     Document Type: Conference Paper

References (24)

1. J. R. Beveridge et al. The csu face identification evaluation system. Machine Vision and Applications, 16, 2004.
2. W. M. Brown, M. George, and A. C. Wilson. Rapid evolution of animal mitochondrial dna. PNAS, 76(4), 1979.
3. K. Chase et al. Genetic basis for systems of skeletal quantitative traits: Principal component analysis of the canid skeleton. PNAS, 99:9930-9935, 2002.
4. C. Goh et al. Co-evolution of proteins with their interaction partners. J. Molecular Biology, 299(2), 2000.
5. D. R. Hardoon and J. Shawe-Taylor. Kcca for different level precision in content-based image retrieval. Int. Workshop on Content-Based Multimedia Indexing, 2003.
6. P. D. N. Hebert et al. Biological identifications through dna barcodes. Proc. Royal Society Biological Sciences, 270(1512), 2003.
7. H. Hotelling. Relations between two sets of variates. Biometrika, 28(3/4):321-377, December 1936.
8. K. C. Kerr et al. Comprehensive dna barcode coverage of north american birds. Molecular Ecology Notes,7(4),2007.
9. M. Kimura. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J Mol Evol, 16(2):111-120, 1980.
10. D. Lin and X. Tang. Inter-modality face recognition. In ECCV, 2006.
11. D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J. Computer Vision, 60(2), 2004.
12. D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In CVPR, 2006.
13. E. Nowak, F. Jurie, and B. Triggs. Sampling strategies for bag-of-features image classification. In ECCV 2006.
14. S. Ratnasingham and P. D. N. Hebert. bold: The barcode of life data system. Molecular Ecology Notes,7(10),2007.
15. T. Sandor Szedmak and D. Hardoon. A metamorphosis of canonical correlation analysis into multivariate maximum margin learning. Euro. Symp. Artificial Neural Nets.,2007.
16. T. Serre et al. Robust object recognition with cortex-like mechanisms. PAMI, 29(3), 2007.
17. M. A. Smith, B. L. Fisher, and P. D. N. Hebert. Dna barcoding for effective biodiversity assessment of a hyperdiverse arthropod group: the ants of madagascar. Phil. Trans. Roy. Soc. Biological Sciences, 360(1462), 2005.
18. R. R. Sokal and C. D. Michner. A statistical method for evaluating systematic relationships. University of Kansas Science Bulletin, 38:1409-1438, 1958.
19. R. A. Sturm and T. N. Frudakis. Eye colour: portals into pigmentation genes and ancestry. Trends in Genetics, 20(8):327-332, 2004.
20. N. B. Sutterm et al. A single igf1 allele is a major determinant of small size in dogs. Science, 316(5821), 2007.
21. G. J. Van Tonder, M. J. Lyons and Y. Ejima. Perception psychology: Visual structure of a Japanese Zen garden. Nature, 19, 2002.
22. H. D. Vinod. Canonical ridge and econometrics of joint production. J. of Econometrics, 4(2):147-166, 1976.
23. R. D. Waard et al. Dna barcoding australia's fish species. Phil. Trans. Roy. Soc. Biological Sciences,360(1462),2005.
24. L. Wolf and A. Shashua. Learning over Sets using Kernel Principal Angles. J. Machine Learning Res., 4(10), 2003.