Comparing state-of-the-art visual features on invariant object recognition tasks

2011 IEEE Workshop on Applications of Computer Vision, WACV 2011

Volumn , Issue , 2011, Pages 463-470

  Pinto, Nicolas ^a   Barhomi, Youssef ^a   Cox, David D ^b   DiCarlo, James J ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL APPROACH; FUNDAMENTAL PROBLEM; IMAGE VARIATIONS; INVARIANT OBJECT RECOGNITION; INVARIANT RECOGNITION; NATURAL IMAGE DATABASE; NATURAL IMAGES; PERFORMANCE GAIN; RECOGNITION SYSTEMS; VISUAL FEATURE; VISUAL OBJECT RECOGNITION; VISUAL REPRESENTATIONS;

COMPUTER APPLICATIONS; COMPUTER VISION; FEATURE EXTRACTION; PROBES;

OBJECT RECOGNITION;

EID: 79952518221     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WACV.2011.5711540     Document Type: Conference Paper

References (37)

1. A. C. Berg and J. Malik. Geometric blur and template matching. CVPR, 2001.
2. A. Bosch, A. Zisserman, and X. Munoz. Representing shape with a spatial pyramid kernel. CIVR, 2007.
3. H. Christensen and P. Phillips. Empirical evaluation methods in computer vision. 2002.
4. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
5. J. J. DiCarlo and D. D. Cox. Untangling invariant object recognition. TICS, 2007.
6. S. Dickinson. The evolution of object categorization and the challenge of image abstraction. Object Categorization: Computer and Human Vision Perspectives., 2009.
7. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2009 (VOC2009) Results.
8. 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. CVPR, 2004.
9. K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun. What is the best multistage architecture for object recognition? In ICCV, 2009.
10. K. Kavukcuoglu, M. Ranzato, R. Fergus, and Y. LeCun. Learning invariant features through topographic filter maps. In CVPR, 2009.
11. S. Kim and I. Kweon. Biologically motivated perceptual feature: Generalized robust invariant feature. ACCV, 2006.
12. S. Lazebnik, C. Schmid, and J. Ponce. Spatial Pyramid Matching. Object Categorization: Computer and Human Vision Perspectives., 2009.
13. Y. LeCun, F. Huang, and L. Bottou. Learning methods for generic object recognition with invariance to pose and lighting. In CVPR, 2004.
14. Y. LeCun, D. G. Lowe, J. Malik, J. Mutch, P. Perona, and T. Poggio. Object recognition, computer vision, and the caltech 101: A response to pinto et al., 2008.
15. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 2004.
16. D. Martin, C. Fowlkes, and J. Malik. Learning to detect natural image boundaries using local brightness, color, and texture cues. PAMI, 2004.
17. C. Mervis and E. Rosch. Categorization of natural objects. Annual review of psychology, 1981.
18. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. PAMI, 2005.
19. P. Moreels and P. Perona. Evaluation of features detectors and descriptors based on 3D objects. ICCV, 2007.
20. H. Murase and S. Nayar. Visual learning and recognition of 3-D objects from appearance. IJCV, 1995.
21. J. Mutch and D. G. Lowe. Object class recognition and localization using sparse features with limited receptive fields. IJCV, 2008.
22. Peter V. Gehler and Sebastian Nowozin. On Feature Combination for Multiclass Object Classification. In ICCV, 2009.
23. N. Pinto, D. D. Cox, and J. J. DiCarlo. Why is Real-World Visual Object Recognition Hard. PLoS Comp. Bio., 2008.
24. N. Pinto, J. J. DiCarlo, and D. D. Cox. Establishing Good Benchmarks and Baselines for Face Recognition. ECCV, 2008.
25. N. Pinto, J. J. DiCarlo, and D. D. Cox. How far can you get with a modern face recognition test set using only simple features? CVPR, 2009.
26. N. Pinto, D. Doukhan, J. J. DiCarlo, and D. D. Cox. A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLoS Comp Bio, 2009.
27. J. Ponce, T. L. Berg, M. Everingham, D. A. Forsyth, M. Hebert, S. Lazebnik, M. Marszalek, C. Schmid, B. C. Russell, A. Torralba, et al. Dataset Issues in Object Recognition. LNCS, 2006.
28. M. Riesenhuber and T. Poggio. Hierarchical models of object recognition in cortex. Nat. Neurosci., 1999.
29. B. Russell, A. Torralba, K. Murphy, and W. Freeman. LabelMe: a database and web-based tool for image annotation. IJCV, 2008.
30. T. Serre, A. Oliva, and T. Poggio. A feedforward architecture accounts for rapid categorization. PNAS, 2007.
31. T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio. Robust object recognition with cortex-like mechanisms. PAMI, 2007.
32. L. Shamir. Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods. IJCV, 2008.
33. S. Ullman. High-level vision: Object recognition and visual cognition. The MIT Press, 2000.
34. K. E. A. van de Sande, T. Gevers, and C. G. M. Snoek. Evaluating color descriptors for object and scene recognition. PAMI, 2010.
35. M. Varma and D. Ray. Learning The Discriminative Power-Invariance TradeOff. ICCV, 2007.
36. H. Zhang, A. Berg, M. Maire, and J. Malik. SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In CVPR, 2006.
37. J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid. Local features and kernels for classification of texture and object categories: A comprehensive study. IJCV, 2007.