A visual category filter for google images

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

Volumn 3021, Issue , 2004, Pages 242-256

  Fergus, R ^a   Perona, P ^b   Zisserman, A ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b California Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; IMAGE RETRIEVAL;

CLUTTERED IMAGES; CONSTELLATION MODEL; IMAGE SEARCH; IMAGE SEARCH ENGINE; OBJECT CATEGORIES; SPATIAL CONFIGURATION; VISUAL CONSISTENCY; VISUAL OBJECTS;

SEARCH ENGINES;

EID: 35048858274     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24670-1_19     Document Type: Article

References (23)

1. Y. Amit and D. Geman. A computational model for visual selection. Neural Computation, 11(7):1691-1715,1999.
2. J. Bach, C. Fuller, R. Humphrey, and R. Jain. The virage image search engine: An open framework for image management. In SPIE Conf. on Storage and Retrieval for Image and Video Databases, volume 2670, pages 76-87,1996.
3. E. Borenstein and S. Ullman. Class-specific, top-down segmentation. In Proc. ECCV, pages 109-124,2002.
4. S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. In 7th Int. WWW Conference, 1998.
5. M. Burl, T. Leung, and P. Perona. A probabilistic approach to object recognition using local photometry and global geometry. In Proc. ECCV, 1998.
6. J. F. Canny. A computational approach to edge detection. IEEEPAMI, 8(6):679-698,1986.
7. T. Deselaers, D. Keysers, and H. Key. Clustering visually similar images to improve image search engines. In Informatiktage 2003 der Gesellschaft für Informatik, Bad Schussenried, Germany., 2003.
8. L. Fei-Fei, R. Fergus, and P. Perona. A bayesian approach to unsupervised one-shot learning of object categories. In Proceedings of the 9th International Conference on Computer Vision, Nice, France, pages 1134-1141,2003.
9. P. Felzenszwalb and D. Huttenlocher. Pictorial structures for object recognition. In Proc. CVPR, pages 2066-2073,2000.
10. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scaleinvariant learning. In Proc. CVPR, 2003.
11. M. A. Fischler and R. C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm.ACM, 24(6):381-395, 1981.
12. T. Gevers and A. W. M. Smeulders. Content-based image retrieval by viewpoint-invariant color indexing. Image and vision computing, 17:475-488,1999.
13. B. Heisele, T. Serre, M. Pontil, T. Vetter, and T. Poggio. Categorization by learning and combining object parts. In Advances in Neural Information Processing Systems 14, Vancouver, Canada., volume 2, pages 1239-1245,2002.
14. T. Kadir and M. Brady. Scale, saliency and image description. IJCV, 45(2):83-105,2001.
15. B. Leibe and B. Schiele. Analyzing appearance and contour based methods for object categorization. In Proc. CVPR, 2003.
16. M. Riesenhuber and T. Poggio. Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11):1019-1025,1999.
17. C. Rothwell, A. Zisserman, D. Forsyth, and J. Mundy. Planar object recognition using projective shape representation. IJCV, 16(2), 1995.
18. C. Schmid. Constructing models for content-based image retrieval. In Proc. CVPR, volume 2, pages 39-45,2001.
19. S. Tong and E. Chang. Support vector machine active learning for image retrieval. ACM Multimedia,2001.
20. N. Vasconcelos and A. Lippman. Learning from user feedback in image retrieval systems. In Proc. ECCV, 2000.
21. R. Veltkamp and M. Tanase. Content-based image retrieval systems: A survey. Technical Report UU-CS-2000-34, Department of Computing Science, Utrecht University, 2000.
22. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proc. CVPR, pages 511-518,2001.
23. M. Weber, M. Welling, and P. Perona. Unsupervised learning of models for recognition. In Proc. ECCV, pages 18-32,2000.