Sparse patch-histograms for object classification in cluttered images

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4174 LNCS, Issue , 2006, Pages 202-211

  Deselaers, Thomas ^a   Hegerath, Andre ^a   Keysers, Daniel ^a   Ney, Hermann ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; IMAGE ANALYSIS; MATHEMATICAL MODELS; RADIOGRAPHY; SIGNAL DETECTION;

HISTOGRAMS; IMAGE PATCHES; INTER-BIN SIMILARITIES; OBJECT DETECTIONS;

OBJECT RECOGNITION;

EID: 33750270983     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11861898_21     Document Type: Conference Paper

References (19)

1. Opelt, A., Pinz, A., Fussenegger, M., Auer, P.: Generic object recognition with boosting. 28(3) (2006) 416-431
2. Fergus, R., Perona, P., Zisserman, A.: A sparse object category model for efficient learning and exhaustive recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 05). (2005) 380-389
3. Ulusoy, I., Bishop, C.M.: Generative versus discriminative methods for object recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 05). (2005) 258-265
4. Marée, R.; Geurts, P., Piater, J.; Wehenkel, L.: Random subwindows for robust image classification. In: IEEE Conference on Computer Vision and Pattern Recognition. (2005) 34-40
5. Deselaers, T., Keysers, D., Ney, H.: Discriminative training for object recognition using image patches. In: IEEE Conference on Computer Vision and Pattern Recognition. Volume 2., San Diego, CA (2005) 157-162
6. Bosch, A., Zissermann, A., Muñoz, X.: Scene classification via plsa. In: ECCV 2006. Volume 3954 of LNCS., Graz, Austria (2006) 517-530
7. Shotton, J., Winn, J., Rother, C., Criminisi, A.: TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: ECCV 2006. Volume 3951 of LNCS., Graz, Austria (2006) 1-15
8. Fergus, R., Perona, P., Zissermann, A.: Object class recognition by unsupervised scale-invariant learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 03), Blacksburg, VG (2003) 264-271
9. Leibe, B., Schiele, B.: Scale invariant object categorization using a scale-adaptive mean-shift search. In: DAGM. Number 3175 in LNCS. (2004) 145-153
10. Linde, O., Lindberg, T.: Object recognition using composed recpetive field histograms of higher dimensionality. In: International Conference on Pattern Recognition, Cambridge, UK (2004)
11. Deselaers, T., Keysers, D., Ney, H.: Improving a discriminative approach to object recognition using image patches. In: DAGM 2005, Pattern Recognition, 26th DAGM Symposium. Number 3663 in Lecture Notes in Computer Science, Vienna, Austria (2005) 326-333
12. Puzicha, J., Rubner, Y., Tomasi, C., Buhmann, J.: Empirical evaluation of dissimilarity measures for color and texture. In: International Conference on Computer Vision. Volume 2., Corfu, Greece (1999) 1165-1173
13. Keysers, D., Gollan, C., Ney, H.: Local context in non-linear deformation models for handwritten character recognition. In: International Conference on Pattern Recognition. Volume 4., Cambridge, UK (2004) 511-514
14. Jeon, J., Manmatha, R.: Using maximum entropy for automatic image annotation. In: Proceedings of the 3rd International Conference on Image and Video Retrieval. (2004) 24-32
15. Darroch, J.N., Ratcliff, D.: Generalized iterative scaling for log-linear models. Annals of Mathematical Statistics 43(5) (1972) 1470-1480
16. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. 2nd edn. Morgan Kaufmann, San Francisco (2005)
17. Clough, P., Mueller, H., Deselaers, T., Grubinger, M., Lehmann, T., Jensen, J., Hersh, W.: The clef 2005 cross-language image retrieval track. In: Working Notes of the CLEF Workshop, Vienna, Austria (2005)
18. Zhang, W., Yu, B., Zelinsky, G.J., Samaras, D.: Object class recognition using multiple layer boosting with heterogeneous features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 05). (2005) 323-330
19. Deselaers, T., Keysers, D., Ney, H.: Classification error rate for quantitative evaluation of content-based image retrieval systems. In: International Conference on Pattern Recognition 2004. Volume 2., Cambridge, UK (2004) 505-508