Harvesting image databases from the Web

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 33, Issue 4, 2011, Pages 754-766

  Schroff, F ^a   Criminisi, A ^b   Zisserman, A ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b MICROSOFT RESEARCH   (United Kingdom)

^c UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

computer vision; image retrieval.; object recognition; Weakly supervised

Indexed keywords

AUTOMATIC RANKING; CROSS VALIDATION; HIGH QUALITY IMAGES; IMAGE DATABASE; MULTI-MODAL APPROACH; NUMBER OF METHODS; OBJECT CLASS; OBJECT IDENTIFIER; RE-RANKING; TRAINING DATA; VISUAL FEATURE; WEAKLY SUPERVISED; WEB PAGE; WEB SEARCHES;

ANIMALS; COMPUTER VISION; IMAGE RETRIEVAL; METADATA; NATURAL LANGUAGE PROCESSING SYSTEMS; WORLD WIDE WEB;

OBJECT RECOGNITION;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; FACTUAL DATABASE; IMAGE ENHANCEMENT; INTERNET; METHODOLOGY;

ALGORITHMS; DATABASES, FACTUAL; IMAGE ENHANCEMENT; INTERNET; PATTERN RECOGNITION, AUTOMATED;

EID: 79951941997     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2010.133     Document Type: Article

References (35)

1. J. Aslam and M. Montague, "Models for Metasearch," Proc. ACM Conf. Research and Development (Pubitemid 32951081)
2. K. Barnard, P. Duygulu, N. de Freitas, D. Forsyth, D. Blei, and M. Jordan, "Matching Words and Pictures," J. Machine Learning Research, vol. 3, pp. 1107-1135, Feb. 2003.
3. T. Berg. Animalson the Web Data Set. http://www.tamaraberg. com/animalDataset/index.html. 2006.
4. T. Berg, A. Berg, J. Edwards, M. Mair, R. White, Y. Teh, E. Learned-Miller, and D. Forsyth, "Names and Faces in the News," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.
5. T.L. Berg and D.A. Forsyth, "Animals on the Web," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
6. D. Blei, A. Ng, and M. Jordan, "Latent Dirichlet Allocation," J. Machine Learning Research, vol. 3, pp. 993-1022, Jan. 2003.
7. C.K. Chow and C.N. Liu, "Approximating Discrete Probability Distributions with Dependence Trees," IEEE Information Theory, vol. 14, no. 3, pp. 462-467, May 1968.
8. B. Collins, J. Deng, K. Li, and L. Fei-Fei, "Towards Scalable Data Set Construction: An Active Learning Approach," Proc. 10th European Conf. Computer Vision, 2008.
9. N. Dalal and B. Triggs, "Histogram of Oriented Gradients for Human Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 886-893, 2005. (Pubitemid 43897286)
10. G. Dorkó and C. Schmid, "Selection of Scale-Invariant Parts for Object Class Recognition," Proc. Ninth Int'l Conf. Computer Vision, 2003.
11. R. Fergus, P. Perona, and A. Zisserman, "A Visual Category Filter for Google Images," Proc. Eighth European Conf. Computer Vision, May 2004.
12. R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman, "Learning Object Categories from Google's Image Search," Proc. 10th Int'l Conf. Computer Vision, 2005.
13. C. Frankel, M.J. Swain, and V. Athitsos, "Webseer: An Image Search Engine for the World Wide Web," technical report, Univ. of Chicago, 1997.
14. M. Fritz and B. Schiele, "Decomposition, Discovery and Detection of Visual Categories Using Topic Models," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
15. T. Hofmann, "Unsupervised Learning by Probabilistic Latent Semantic Analysis," Machine Learning, vol. 43, pp. 177-196, 2001. (Pubitemid 32872403)
16. T. Hofmann, "Probabilistic Latent Semantic Analysis," Proc. Conf. Uncertainty in Artificial Intelligence, 1999.
17. light," http://svmlight.joachims. org/, 2010.
18. T. Kadir, A. Zisserman, and M. Brady, "An Affine Invariant Salient Region Detector," Proc. Eighth European Conf. Computer Vision, May 2004.
19. J. Li, G. Wang, and L. Fei-Fei, "OPTIMOL: Automatic Object Picture Collection via Incremental Model Learning," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
20. W.-H. Lin, R. Jin, and A. Hauptmann, "Web Image Retrieval Re-Ranking with Relevance Model," Proc. IADIS Int'l Conf. WWW/ Internet, 2003.
21. D. Lowe, "Object Recognition from Local Scale-Invariant Features," Proc. Seventh IEEE Int'l Conf. Computer Vision, pp. 1150-1157, Sept. 1999. (Pubitemid 32079997)
22. K. Mikolajczyk, C. Schmid, and A. Zisserman, "Human Detection Based on a Probabilistic Assembly of Robust Part Detectors," Proc. Eighth European Conf. Computer Vision, May 2004.
23. K. Morik, P. Brockhausen, and T. Joachims, "Combining Statistical Learning with a Knowledge-Based Approach-A Case Study in Intensive Care Monitoring," Proc. 16th Int'l Conf. Machine Learning, 1999.
24. Onix. ONIX Text Retrieval Toolkit. http://www.lextek.com/ manuals/onix/stopwords1.html. 2010.
25. M. Porter, R. Boulton, and A. Macfarlane, "The English (Porter2) Stemming Algorithm," http://snowball.tartarus.org/, 2002.
26. K. Saenko and T. Darrell, "Unsupervised Learning of Visual Sense Models for Polysemous Words," Proc. Conf. Advances in Neural Information Processing Systems, 2008.
27. F. Schroff, "Semantic Image Segmentation and Web-Supervised Visual Learning," DPhil thesis, Univ. of Oxford, 2009.
28. F. Schroff, A. Criminisi, and A. Zisserman, "Harvesting Image Databases from the Web," Proc. 11th Int'l Conf. Computer Vision, 2007.
29. F. Schroff, A. Criminisi, and A. Zisserman, "Harvesting Image Databases from the Web," http://www.robots.ox.ac.uk/~vgg/ data/mkdb, 2007.
30. Y. Teh, M. Jordan, M. Beal, and D. Blei, "Hierarchical Dirichlet Processes," Technical Report 653, 2003.
31. A. Torralba, "Contextual Priming for Object Detection," Int'l J. Computer Vision, vol. 53, no. 2, pp. 153-167, 2003.
32. VGG. Affine Covariant Features. http://www.robots.ox.ac.uk/ ~vgg/research/affine/index.html. 2010.
33. S. Vijayanarasimhan and K. Grauman, "Keywords to Visual Categories: Multiple-Instance Learning for Weakly Supervised Object Categorization," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
34. G. Wang and D. Forsyth, "Object Image Retrieval by Exploiting Online Knowledge Resources," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2008.
35. J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, "Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study," Int'l J. Computer Vision, vol. 73, no. 2, pp. 213-238, 2007. (Pubitemid 46181625)