Attribute-based detection of unfamiliar classes with humans in the loop

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2013, Pages 779-786

  Wah, Catherine ^a   Belongie, Serge ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

attribute based classification; fine grained visual categories; human in the loop; unfamiliar class detection; visual recognition

Indexed keywords

ATTRIBUTE DETECTIONS; ATTRIBUTE-BASED; CLASSIFICATION ACCURACY; CLASSIFICATION METHODS; FINE-GRAINED VISUAL CATEGORIES; HUMAN-IN-THE-LOOP; VISION COMMUNITIES; VISUAL RECOGNITION;

PATTERN RECOGNITION;

CLASSIFICATION (OF INFORMATION);

EID: 84887334314     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.106     Document Type: Conference Paper

References (39)

1. S. Bengio, J. Weston, and D. Grangier. Label embedding trees for large multi-class tasks. In NIPS, 2010.
2. T. Berg, A. Berg, and J. Shih. Automatic attribute discovery and characterization from noisy web data. In ECCV, 2010.
3. A. Bergamo et al. Picodes: Learning a compact code for novelcategory recognition. In NIPS, 2011.
4. I. Biederman. Recognition by components: a theory of human image interpretation. Pyschological review, 94:115-147, 1987.
5. P. Bodesheim et al. Divergence-based one-class classification using gaussian processes. In BMVC, 2012.
6. S. Branson et al. Visual recognition with humans in the loop. In ECCV, 2010.
7. J. Deng et al. ImageNet: A large-scale hierarchical image database. In CVPR, 2009.
8. J. Deng et al. Fast and balanced: Efficient label tree learning for large scale object recognition. In NIPS, 2011.
9. T. Dietterich and G. Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2:263-286, 1995.
10. M. Everingham et al. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://pascallin. ecs. soton. ac. uk/challenges/VOC/voc2012/ index. html.
11. A. Farhadi, I. Endres, and D. Hoiem. Attribute-centric recognition for cross-category generalization. In CVPR, 2010.
12. A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth. Describing objects by their attributes. In CVPR, 2009.
13. L. Fei-Fei et al. Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. In IEEE CVPR Workshops, 2004.
14. G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical Report CNS-TR-2007-001, California Institute of Technology, 2007.
15. G. Griffin and P. Perona. Learning and using taxonomies for fast visual categorization. In CVPR, 2008.
16. N. Kumar et al. Attribute and simile classifiers for face verification. In ICCV, 2009.
17. N. Kumar et al. Leafsnap: A computer vision system for automatic plant species identification. In The 12th European Conference on Computer Vision (ECCV), October 2012.
18. C. H. Lampert et al. Learning to detect unseen object classes by between-class attribute transfer. In CVPR, 2009.
19. Y. J. Lee and K. Grauman. Object-graphs for context-aware category discovery. In CVPR, 2010.
20. D. Mahajan et al. A joint learning framework for attribute models and object descriptions. In CVPR, 2011.
21. M. Markou and S. Singh. Novelty detection: a review-part 1: statistical approaches. Signal Processing, 83:2481-2497, 2003.
22. M. Markou and S. Singh. Novelty detection: a review-part 2: neural network based approaches. Signal Processing, 83:2499-2521, 2003.
23. G. Martinez-Muñoz et al. Dictionary-free categorization of very similar objects via stacked evidence trees. In CVPR, 2009.
24. M. Nilsback and A. Zisserman. Automated flower classification over a large number of classes. In ICCVGIP, 2008.
25. M. Palatucci et al. Zero-Shot Learning with Semantic Output Codes. In NIPS, 2009.
26. D. Parikh and K. Grauman. Interactively building a discriminative vocabulary of nameable attributes. In CVPR, 2011.
27. D. Parikh and K. Grauman. Relative attributes. In ICCV, 2011.
28. P. Perona. Vision of a visipedia. Proceedings of the IEEE, 98(8):1526-1534, aug. 2010.
29. J. Platt. Probabilities for SV machines. In NIPS, pages 61-74, 1999.
30. J. Ponce et al. Dataset issues in object recognition. In Towards Category-Level Object Recognition, pages 29-48. Springer, 2006.
31. M. Rohrbach et al. What helps where-and why? semantic relatedness for knowledge transfer. In CVPR, 2010.
32. B. C. Russell et al. LabelMe: A database and web-based tool for image annotation. IJCV, 77(1-3):157-173, 2008.
33. W. J. Scheirer, A. Bendale, and T. E. Boult. Predicting biometric facial recognition failure with similarity surfaces and support vector machines. In IEEE CVPR Workshops, 2008.
34. B. Schölkopf, J. Platt, J. Shawe-Taylor, A. Smola, and R. Williamson. Estimating the support of a high-dimension distribution. Neural Computation, 13(7):1442-1471, 2001.
35. A. Torralba and A. A. Efros. Unbiased look at dataset bias. In CVPR, 2011.
36. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman. Multiple kernels for object detection. In ICCV, 2009.
37. C. Wah et al. Multiclass recognition and part localization with humans in the loop. In ICCV, 2011.
38. C. Wah et al. The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001, Caltech, 2011.
39. J. Wang et al. Learning models for object recognition from natural language descriptions. In BMVC, 2009.