Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Tsai, C F; McGarry, Kenneth; Tait, John (2004)
Languages: English
Types: Unknown
Subjects: sub_informationsystems
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Aksoy, S. and Haralick, R.M. (2000) Using texture in image similarity and retrieval. In Texture Analysis in Machine Vision, Pietikainen, M. and Bunke, H. (Eds.), vol. 20, World Scientific, Singapore, pp. 129-149.
    • [2] Antani, S., Kasturi, R., and Jain, R. (2002) A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video. Pattern Recognition, vol. 35, pp. 945-965.
    • [3] Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D.M., Jordan, M.I. (2003) Matching Words and Pictures. Journal of Machine Learning Research, vol. 3, pp. 1107- 1135.
    • [4] Berner-Lee, T., Hendler, J. and Lassila, O. (2001) The Semantic Web. Scientific American, May 17.
    • [5] Bishop, C.M. (1995) Neural networks for pattern recognition. Oxford University Press, Oxford.
    • [6] Campbell, N.W. and Thomas, B.T. (1997) Automatic segmentation and classification of outdoor images using neural networks. International Journal of Neural Systems, vol. 8, no. 1, pp. 137-144.
    • [7] Chan, P.K. and Stolfo, S.J. (1995) Comparative evaluation of voting and meta-learning on partitioned data. Proceedings of the 12th International Conference on Machine Learning, Tahoe City, California, July 9-12, pp. 90-98.
    • [8] Chang, E., Kingshy, G., Sychay, G., and Wu, G. (2003) CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines. IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Conceptual and Dynamical Aspects of Multimedia Content Description, vol. 13, no. 1, pp. 26-38.
    • [9] Chapelle, O., Haffner, P., and Vapnik, V.N. (1999) Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 1055-1064.
    • [10] Ciocca, G., Cusano, C., Schettini, R., and Brambilla, C. (2003) Semantic labeling of digital photos by classification. Proceedings of Internet Imaging IV, SPIE 5018, Santa Clara, CA, Jan. 20-24.
    • [11] Cortes, C. and Vapnik, V. (1995) Support vector networks. Machine Learning, vol. 20, pp. 273-297.
    • [12] Daubechies, I. (1992) Ten lectures on wavelets. Capital City Press, Vermont.
    • [13] Hong, P., Tian, Q., Huang, T.S. (2000) Incorporate support vector machines to content-based image retrieval with relevance feedback. Proceedings of the IEEE International Conference on Image Processing, vol. 3, Vancouver, Canada, Sep. 10-13, pp. 750-753.
    • [14] Horrocks, I and Patel-Schneider, P.F. (2003) Three thesis of representation of the semantic web. Proceedings of the 12th ACM International World Wide Web Conference, Budapest Hungary, May, 20-24, pp. 331-339.
    • [15] Horrocks, I. Patel-Schneider, P.F., van Harmelen, F. (2003) From SHIQ and RDF to OWL: the making of a web ontology language. Journal of Web Semantics, Volume 1, Issue 1, pp. 7-26.
    • [16] Huang, Y., Chan, K.L., and Zhang, Z. (2003) Texture classification by multi-model feature integration using Bayesian networks. Pattern Recognition Letters, vol. 24, pp. 393-401.
    • [17] Hsu, C.-W. and Lin, C.-J. (2002) A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, vol. 12, pp. 1288-1298.
    • [18] Iyatomi, H. and Hagiwara, M. (2002) Scenery image recognition and interpretation using fuzzy inference neural networks. Pattern Recognition, vol. 35, no. 8, pp. 1793-1806.
    • [19] Iyengar, G., Nock, H.J., and Neti, C. (2003) Discriminative model fusion for semantic concept detection and annotation in video. Proceedings of the 11th ACM International Conference on Multimedia, Berkeley, CA, Nov. 2-8, pp. 255- 258.
    • [20] Jeon, J., Lavrenko, V., and Manmatha, R. (2003) Automatic image annotation and retrieval using cross-media relevance models. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, Canada, July 28-Aug. 1, pp. 119-126.
    • [21] Kuroda, K. and Hagiwara, M. (2002) An image retrieval system by impression words and specific object names - IRIS. Neurocomputing, vol. 43, no. 1-4, pp. 259-276.
    • [22] Lai, T.-S. and Tait, J. (2000) CHROMA: a content-based image retrieval system. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, California, Aug. 15-19, pp. 324.
    • [23] Li, S., Kwok, J.T., Zhu, H., and Wang, Y. (2003) Texture classification using support vector machines. Pattern Recognition, vol. 36, no. 12, pp. 2883-2893.
    • [24] Lin, W.-H. and Hauptmann, A. (2002) News video classification using SVM-based multimodal classifiers and combination strategies. Proceedings of the 10th ACM International Conference on Multimedia, Juan les Pins, France, Dec. 1-6, pp. 323-326.
    • [25] Monay, F. and Gatica-Perez, D. (2003) On image autoannotation with latent space models. Proceedings of the 11th ACM International Conference on Multimedia, Berkeley, CA, Nov. 2-8, pp. 275-278.
    • [26] Osuna, E., Freund, R., Girosi, F. (1997) Training support vector machines: an application to face detection. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Puerto Rico,June 17-19, pp. 130-136.
    • [27] Pagano, R.R. (2001) Understanding statistics in the behavioral sciences, Sixth Edition. Wadsworth/Thomson Learning, California.
    • [28] Schapire, R.E., Freund, Y., Bartlett, P., and Lee, W.S. (1997) Boosting the margin: a new explanation for the effectiveness of voting methods. Proceedings of the 14th International Conference on Machine Learning, Nashville, Tennessee, USA, July 8-12, pp. 322-330.
    • [29] Serrano, N., Savakis, A., and Luo, J. (2002) A computationally efficient approach to indoor/outdoor scene classification. Proceedings of the IEEE International Conference on Pattern Recognition, Quebec, Canada, Aug. 11-15, pp. 146-149.
    • [30] Sheikholeslami, G., Chang, W., and Zhang, A. (1998) SemQuery: semantic clustering and querying on heterogeneous features for visual data. IEEE Transactions on Knowledge and Data Engineering, vol. 14, no. 5, pp. 988- 1002.
    • [31] Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., and Jain, R. (2000) Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380.
    • [32] Szummer, M. and Picard, R.W. (1998) Indoor-outdoor image classification. Proceedings of the IEEE International Workshop on Content-based Access of Image and Video Databases, Bombay, India, Jan. 3, pp. 42-51.
    • [33] Tsai, C.-F., McGarry, K., and Tait, J. (2003) Image classification using hybrid neural networks. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, Canada, July 28-Aug. 1, pp. 431-432.
    • [34] Tong, S. and Chang, E. (2001) Support vector machine active learning for image retrieval. Proceedings of the ACM International Conference on Multimedia, Ottawa, Ontario, Canada, Sep. 30-Oct. 5, pp. 107-118.
    • [35] Vailaya, A., Figueiredo, M.A.T., Jain, A.K., and Zhang, H.- J. (2001) Image classification for content-based indexing. IEEE Transactions on Image Processing, vol. 10, no. 1, pp. 117-130.
    • [36] Vapnik, V. (1998) Statistical learning theory. John Wiley, New York.
    • [37] Wolpert, D.H. (1992) Stacked generalization. Neural Networks, vol. 5, no. 2, pp. 241-259.
    • [38] WordNet [On-line] Available from: http://www.cogsci.princeton.edu/~wn/
    • [39] Wyszecki, G. and Stiles, W.S. (2000) Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition. John Wiley & Sons.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article