Abstract
We extend the constellation model to include heterogeneous parts which may represent either the appearance or the geometry of a region of the object. The parts and their spatial configuration are learnt simultaneously and automatically, without supervision, from cluttered images.
We describe how this model can be employed for ranking the output of an image search engine when searching for object categories. It is shown that visual consistencies in the output images can be identified, and then used to rank the images according to their closeness to the visual object category.
Although the proportion of good images may be small, the algorithm is designed to be robust and is capable of learning in either a totally unsupervised manner, or with a very limited amount of supervision.
We demonstrate the method on image sets returned by Google’s image search for a number of object categories including bottles, camels, cars, horses, tigers and zebras.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Amit, Y., Geman, D.: A computational model for visual selection. Neural Computation 11(7), 1691–1715 (1999)
Bach, J., Fuller, C., Humphrey, R., Jain, R.: The virage image search engine: An open framework for image management. In: SPIE Conf. on Storage and Retrieval for Image and Video Databases, vol. 2670, pp. 76–87 (1996)
Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 109–124. Springer, Heidelberg (2002)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: 7th Int. WWW Conference (1998)
Burl, M., Leung, T., Perona, P.: A probabilistic approach to object recognition using local photometry and global geometry. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, p. 628. Springer, Heidelberg (1998)
Canny, J.F.: A computational approach to edge detection. IEEE PAMI 8(6), 679–698 (1986)
Deselaers, T., Keysers, D., Ney, H.: Clustering visually similar images to improve image search engines. In: Informatiktage 2003 der Gesellschaft fĂĽr Informatik, Bad Schussenried, Germany (2003)
Fei-Fei, L., Fergus, R., Perona, P.: A bayesian approach to unsupervised one-shot learning of object categories. In: Proceedings of the 9th International Conference on Computer Vision, Nice, France, pp. 1134–1141 (2003)
Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. In: Proc. CVPR, pp. 2066–2073 (2000)
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scaleinvariant learning. In: Proc. CVPR (2003)
Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24(6), 381–395 (1981)
Gevers, T., Smeulders, A.W.M.: Content-based image retrieval by viewpoint-invariant color indexing. Image and vision computing 17, 475–488 (1999)
Heisele, B., Serre, T., Pontil, M., Vetter, T., Poggio, T.: Categorization by learning and combining object parts. In: Advances in Neural Information Processing Systems 14, Vancouver, Canada, vol. 2, pp. 1239–1245 (2002)
Kadir, T., Brady, M.: Scale, saliency and image description. IJCV 45(2), 83–105 (2001)
Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: Proc. CVPR (2003)
Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nature Neuroscience 2(11), 1019–1025 (1999)
Rothwell, C., Zisserman, A., Forsyth, D., Mundy, J.: Planar object recognition using projective shape representation. IJCVÂ 16(2) (1995)
Schmid, C.: Constructing models for content-based image retrieval. In: Proc. CVPR, vol. 2, pp. 39–45 (2001)
Tong, S., Chang, E.: Support vector machine active learning for image retrieval. ACM Multimedia (2001)
Vasconcelos, N., Lippman, A.: Learning from user feedback in image retrieval systems. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 33–47. Springer, Heidelberg (2000)
Veltkamp, R., Tanase, M.: Content-based image retrieval systems: A survey. Technical Report UU-CS-2000-34, Department of Computing Science, Utrecht University (2000)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. CVPR, pp. 511–518 (2001)
Weber, M., Welling, M., Perona, P.: Unsupervised learning of models for recognition. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 18–32. Springer, Heidelberg (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fergus, R., Perona, P., Zisserman, A. (2004). A Visual Category Filter for Google Images. In: Pajdla, T., Matas, J. (eds) Computer Vision - ECCV 2004. ECCV 2004. Lecture Notes in Computer Science, vol 3021. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24670-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-24670-1_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21984-2
Online ISBN: 978-3-540-24670-1
eBook Packages: Springer Book Archive