Automated insect identification through concatenated histograms of local appearance features: Feature vector generation and region detection for deformable objects

Machine Vision and Applications

Volumn 19, Issue 2, 2008, Pages 105-123

  Larios, Natalia ^a   Deng, Hongli ^b   Zhang, Wei ^b   Sarpola, Matt ^b   Yuen, Jenny ^d   Paasch, Robert ^b   Moldenke, Andrew ^c   Lytle, David A ^c   Correa, Salvador Ruiz ^e   Mortensen, Eric N ^b   Shapiro, Linda G ^a   Dietterich, Thomas G ^b  

^a University of Washington   (United States)

^b Oregon State University   (United States)

^c OREGON STATE UNIVERSITY   (United States)

^d MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^e CHILDREN'S NATIONAL MEDICAL CENTER   (United States)

Author keywords

Classification; Interest operators; Object recognition; Region detectors; SIFT descriptor

Indexed keywords

COMPUTER VISION; IDENTIFICATION (CONTROL SYSTEMS); OBJECT RECOGNITION; THROUGHPUT; VECTORS;

DEFORMABLE OBJECTS; INTEREST OPERATORS; REGION DETECTORS; SIFT DESCRIPTORS;

FEATURE EXTRACTION;

EID: 38749151918     PISSN: 09328092     EISSN: 14321769     Source Type: Journal    
DOI: 10.1007/s00138-007-0086-y     Document Type: Article

References (45)

1. Arbuckle, T., Schroder, S., Steinhage, V., Wittmann, D.: Biodiversity informatics in action: identification and monitoring of bee species using ABIS. In: Proceedings of the 15th International Symposium Informatics for Environmental Protection, vol. 1, pp. 425-430. Zurich (2001)
2. Bouchard, G., Triggs, B.: Hierarchical part-based visual object categorization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. I 710-715 (2005). http://lear.inrialpes.fr/pubs/2005/BT05
3. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123-140 (1996). http://citeseer.ist.psu.edu/breiman96bagging.html
4. Breiman L., Friedman J., Olshen R. and Stone C. (1984). Classification and Regression Trees. Chapman and Hall, New York
5. Burl, M., Weber, M., Perona, P.: A probabilistic approach to object recognition using local photometry and global geometry. In: Proceedings of the ECCV, pp. 628-641 (1998)
6. Carter J., Resh V., Hannaford M. and Myers M. (2006). Macroinvertebrates as biotic indicators of env. qual. In: Hauer, F. and Lamberti, G. (eds) Methods in Stream Ecology, pp 1-2. Academic, San Diego
7. Csurka, G., Dance, C., Fan, L., Williamowski, J., Bray, C.: Visual categorization with bags of keypoints. ECCV'04 workshop on Statistical Learning in Computer Vision, pp. 59-74 (2004)
8. Csurka, G., Bray, C., Fan, C.L.: Visual categorization with bags of keypoints. ECCV workshop (2004)
9. Do M., Harp J. and Norris K. (1999). A test of a pattern recognition system for identification of spiders. Bull. Entomol. Res. 89(3): 217-224
10. Dorko, G., Schmid, C.: Object class recognition using discriminative local features. INRIA-Rhone-Alpes, RR-5497, February, 2005, Rapport de recherche. http://lear.inrialpes.fr/pubs/ 2005/DS05a
11. Dorkó, G., Schmid, C.: Object class recognition using discriminative local features (2005). http://lear.inrialpes.fr/pubs/2005/DS05. Accepted under major revisions to IEEE Trans. Pattern Anal. Mach. Intell. (updated 13 September)
12. Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol.~2, pp. 264-271. Madison, Wisconsin (2003)
13. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, pp. 148-156 (1996). http://citeseer.ist.psu.edu/freund96experiments.html
14. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting (1998). http://citeseer.ist.psu. edu/friedman98additive.html
15. Gaston K.J. and O'Neill M.A. (2004). Automated species identification: why not?. Philosophical Trans. R. Soc. B: Biol. Sci. 359(1444): 655-667
16. Harris, C., Stephens, M.: A combined corner and edge detector. Alvey Vision Conference, pp. 147-151 (1988)
17. Hilsenhoff W.L. (1988). Rapid field assessment of organic pollution with a family level biotic index. J. North Am. Benthol. Soc. 7: 65-68
18. Hopkins G.W. and Freckleton R.P. (2002). Declines in the numbers of amateur and professional taxonomists: implications for conservation. Anim. Conserv. 5(3): 245-249
19. Jurie F. and Schmid C. (2004). Scale-invariant shape features for recognition of object categories. CVPR 2: 90-96
20. Jurie, F., Triggs, B.: Creating efficient codebooks for visual recognition. In: ICCV '05: Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV'05), vol. 1, pp. 604-610. IEEE Computer Society, Washington, DC, USA (2005). DOI http://dx.doi.org/10.1109/ICCV.2005.66
21. Kadir, T., Zisserman, A., Brady, M.: An affine invariant salient region detector. In: European Conference on Computer Vision (ECCV04), pp. 228-241 (2004)
22. Kumar, S., August, J., Hebert, M.: Exploiting inference for approximate parameter learning in discriminative fields: an empirical study. In: 5th International Workshop, EMMCVPR 2005, pp. 153-168. Springer, St. Augustine (2005)
23. Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Mach. Learn. 59(1-2), 161-205 (2005). DOI 10.1007/s10994-005- 0466-3
24. Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: CVPR '05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, pp. 878-885. IEEE Computer Society, Washington, DC, USA (2005). DOI http://dx.doi.org/10.1109/ CVPR.2005.272
25. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91-110 (2004). DOI 10.1023/B:VISI.0000029664.99615. 94
26. Lucas, S.: Face recognition with continuous n-tuple classifier. In: Proceedings of the British Machine Vision Conference, pp. 222-231. Essex (1997)
27. Matas J., Chum O., Urban M. and Pajdla T. (2004). Robust wide- baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10): 761-767
28. Mikolajczyk K. and Schmid C. (2002). An affine invariant interest point detector. ECCV 1(1): 128-142
29. Mikolajczyk K. and Schmid C. (2004). Scale and affine invariant interest point detectors. IJCV 60(1): 63-86
30. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. IJCV 65(1/2), 43-72 (2005). http://lear.inrialpes.fr/pubs/2005/ MTSZMSKG05
31. O'Neill, M.A., Gauld, I.D., Gaston, K.J., Weeks, P.: Daisy: an automated invertebrate identification system using holistic vision techniques. In: Proceedings of the Inaugural Meeting BioNET-INTERNATIONAL Group for Computer-Aided Taxonomy (BIGCAT), pp. 13-22. Egham (2000)
32. Opelt, A., Fussenegger, M., Pinz, A., Auer, P.: Weak hypotheses and boosting for generic object detection and recognition. In: 8th European Conference on Computer Vision, vol. 2, pp. 71-84. Prague, Czech Republic (2004)
33. Opelt A., Pinz A., Fussenegger M. and Auer P. (2006). Generic object recognition with boosting. IEEE Trans. Pattern Anal. Mach. Intell. 28(3): 416-431
34. Papageorgiou C. and Poggio T. (2000). A trainable system for object detection. Int. J. Comput. Vis. 38(1): 15-33
35. Quattoni, A., Collins, M., Darrell, T.: Conditional random fields for object recognition. In: Proceedings of the NIPS 2004. MIT Press, Cambridge (2005)
36. Quinlan J.R. (1993). C4.5: programs for machine learning. Morgan Kaufmann, San Francisco
37. Shotton, J., Blake, A., Cipolla, R.: Contour-based learning for object detection. In: ICCV '05: Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV'05), vol. 1, pp. 503-510. IEEE Computer Society, Washington, DC, USA (2005). DOI http://dx.doi.org/10.1109/ICCV.2005.63
38. Sokal, R.R., Rohlf, F.J.: Biometry, 3rd edn. W. H. Freeman, Gordonsville (1995)
39. Steger C. (1998). An unbiased detector of curvilinear structures. PAMI 20(2): 113-125
40. Sung K.K. and Poggio T. (1998). Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1): 39-51
41. Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 586-591 (1991)
42. Tuytelaars, T., Gool, L.V.: Wide baseline stereo matching based on local, affinely invariant regions. BMVC, pp. 412-425 (2000)
43. Tuytelaars T. and Gool L.V. (2004). Matching widely separated views based on affine invariant regions. IJCV 59(1): 61-85
44. Vincent L. and Soille P. (1991). Watersheds in digital spaces: an efficient algorithm based on immersion simulations. PAMI 13(6): 583-598
45. Zhang, W., Deng, H., Dietterich, T.G., Mortensen, E.N.: A hierarchical object recognition system based on multi-scale principal curvature regions. International Conference of Pattern Recognition, pp. 1475-1490 (2006)