Category Discovery from the Web slide credit Fei-Fei et. al.

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Category Discovery from the Web slide credit Fei-Fei et. al.

Transcript of Category Discovery from the Web slide credit Fei-Fei et. al.

Page 1: Category Discovery from the Web slide credit Fei-Fei et. al.

Category Discovery from the Web

slide credit Fei-Fei et. al.

Page 2: Category Discovery from the Web slide credit Fei-Fei et. al.

How many object categories are there?

Biederman 1987slide credit Fei-Fei et. al.

Page 3: Category Discovery from the Web slide credit Fei-Fei et. al.

Existing datasets

Datasets # of categories

# of imagesper category

# of total images

Collected by

Caltech101 101 ~100 ~10K Human

Lotus Hill ~300 ~ 500 ~150K Human

LabelMe 183 ~200 ~30K Human

Ideal ~30K >>10^2 A LOT Machine

slide credit Fei-Fei et. al.

Page 4: Category Discovery from the Web slide credit Fei-Fei et. al.

Talk Outline

• Image-only pLSA variant [Fergus05]

• Image-only HDP (OPTIMOL) [Li07]

• Text and image clustering [Berg06]

• Metadata-based re-ranking [Schroff07]

• Dictionary sense models [Saenko08]

Microsoft PowerPoint Presentation

Microsoft PowerPoint Presentation

Microsoft PowerPoint Presentation

Microsoft PowerPoint Presentation

Microsoft PowerPoint Presentation

Page 5: Category Discovery from the Web slide credit Fei-Fei et. al.

Summary

• The web contains unlimited, but extremely noisy object category data

• The text surrounding the image on the web page is an important recognition cue

• Topic models (pLSA, LDA, HDP, etc.) are useful for discovering objects in images and object senses in text

• Different ways to bootstrap model from small amount of labeled or weakly labeled data

• Still an open research problem!

Page 6: Category Discovery from the Web slide credit Fei-Fei et. al.

Bibliography• R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman, "Learning object

categories from Google's image search," ICCV vol. 2, 2005, pp.1816-1823 Vol. 2.  http://dx.doi.org/10.1109/ICCV.2005.142

• T. Berg and D. Forsyth, "Animals on the Web". In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, Washington, DC, 1463-1470. http://dx.doi.org/10.1109/CVPR.2006.57

• L.-J. Li, G. Wang, and L. Fei-Fei, "Optimol: automatic online picture collection via incremental model learning," in Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on, 2007, pp. 1-8.  http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4270073

• F. Schroff, A. Criminisi, and A. Zisserman, "Harvesting image databases from the web," in Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, 2007, pp. 1-8.  http://dx.doi.org/10.1109/ICCV.2007.4409099

• K. Saenko and T. Darrell, "Unsupervised Learning of Visual Sense Models for Polysemous Words". Proc. NIPS, December 2008, Vancouver, Canada. http://people.csail.mit.edu/saenko/saenko_nips08.pdf

http://dx.doi.org/10.1109/ICCV.2005.142
http://dx.doi.org/10.1109/CVPR.2006.57
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4270073
http://dx.doi.org/10.1109/ICCV.2007.4409099
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Additional reading• N.Loeff, C.O. Alm, D.A. Forsyth, “Discriminating image senses by clustering

with multimodal features”. Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pages547–554, Sydney, July 2006 [PDF]

• G. Wang and D. Forsyth, "Object image retrieval by exploiting online  knowledge resources".  IEEE Computer Vision and Pattern Recognition (CVPR). 2008. [PDF]

• D. M. Blei and M. I. Jordan, "Modeling annotated data," in SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval.    New York, NY, USA: ACM Press, 2003, pp. 127-134. http://dx.doi.org/10.1145/860435.860460

• P. Duygulu, K. Barnard, J. F. G. de Freitas, and D. A. Forsyth, "Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary," in ECCV '02: Proceedings of the 7th European Conference on Computer Vision-Part IV.    London, UK: Springer-Verlag, 2002, pp. 97-112. http://portal.acm.org/citation.cfm?id=645318.649254


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