Can I make a wish?: a competition on detecting meteors in images
Resumo
Promoting competitions has become a path towards attracting people’s interest into diverse areas. Many international conferences have sessions dedicated to one or more competitions, in which participants are challenged by real problems for which advanced solutions are needed. This paper describes the first Brazilian competition on Knowledge Discovery in Databases (KDD-BR), which was part of three main events of the Brazilian Computer Society dedicated to Artificial Intelligence, Databases and Data Mining. In this first edition the participants were supposed to detect meteors, popularly known as shooting stars, in regions of interest of images collected from a monitoring station located at São José dos Campos, Brazil. The data set assembled is detailed, which may be of interest for future benchmark studies using such data. The competition results, contributions and limitations are also discussed, providing a guide for future editions.
Palavras-chave:
competition, data mining, machine learning
Referências
Abo-Zaid, A., Hinton, O. R., and Horne, E. About moment normalization and complex moment descriptors. In Pattern Recognition. Springer, pp. 399–409, 1988.
Bosch, A., Zisserman, A., and Munoz, X. Representing shape with a spatial pyramid kernel. In Proceedings of the 6th ACM International Conference on Image and Video Retrieval. ACM, pp. 401–408, 2007.
Carpenter, J. May the best analyst win. Science 331 (6018): 698–699, 2011.
Chatzichristofis, S. A. and Boutalis, Y. S. Cedd: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. In Int. Conf. on Computer Vision Systems. Springer, pp. 312–322, 2008a.
Chatzichristofis, S. A. and Boutalis, Y. S. Fcth: Fuzzy color and texture histogram-a low level feature for accurate image retrieval. In Image Analysis for Multimedia Interactive Services, WIAMIS’08. IEEE, pp. 191–196, 2008b.
Chodas, P. Overview of the jpl center for neo studies (cneos). In AAS/Division for Planetary Sciences Meeting Abstracts. Vol. 47, 2015.
Fernández, A., García, S., del Jesus, M. J., and Herrera, F. A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets. Fuzzy Sets and Systems 159 (18): 2378–2398, 2008.
Fogel, I. and Sagi, D. Gabor filters as texture discriminator. Biological cybernetics 61 (2): 103–113, 1989.
Guyon, I., Cawley, G., Dror, G., and Saffari, A. Hands-on pattern recognition challenges in machine learning, volume, 2011.
Han, J. and Ma, K.-K. Fuzzy color histogram and its use in color image retrieval. IEEE transactions on image processing 11 (8): 944–952, 2002.
Haralick, R. M., Shanmugam, K., et al. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics (6): 610–621, 1973.
Huang, J., Kumar, S. R., Mitra, M., Zhu, W.-J., and Zabih, R. Image indexing using color correlograms. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp. 762–768, 1997.
Jenniskens, P. Meteor showers in review. Planetary and Space Science vol. 143, pp. 116–124, 2017.
Kriegel, H.-P., Schubert, E., and Zimek, A. Evaluation of multiple clustering solutions. In MultiClust@ ECML/PKDD. pp. 55–66, 2011.
Milano-Oliveira, L. F. and Kaster, D. Defining similarity spaces for large-scale image retrieval through scientific workflows. In Proceedings of the 21st Int. Database Engineering & Applications Symposium. ACM, pp. 57–65, 2017.
Novak, C. L. and Shafer, S. A. Anatomy of a color histogram. In Computer Vision and Pattern Recognition, 1992. Proceedings CVPR’92., 1992 IEEE Computer Society Conference on. IEEE, pp. 599–605, 1992.
Ojala, T., Pietikainen, M., and Harwood, D. Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In Proceedings of the 12th IAPR International Conference on Pattern Recognition. Vol. 1. IEEE, pp. 582–585, 1994.
Rosset, S. and Inger, A. Kdd-cup 99: knowledge discovery in a charitable organization’s donor database. SIGKDD Explorations 1 (2): 85–90, 2000.
Scott, D. W. Averaged shifted histogram. Wiley Interdisciplinary Reviews: Computational Statistics 2 (2): 160–164, 2010.
Sikora, T. The mpeg-7 visual standard for content description-an overview. IEEE Transactions on circuits and systems for video technology 11 (6): 696–702, 2001.
Tamura, H., Mori, S., and Yamawaki, T. Textural features corresponding to visual perception. IEEE Transactions on Systems, man, and cybernetics 8 (6): 460–473, 1978.
Taylor, S. and Drummond, T. Binary histogrammed intensity patches for efficient and robust matching. International journal of computer vision 94 (2): 241–265, 2011.
Van De Sande, K., Gevers, T., and Snoek, C. Evaluating color descriptors for object and scene recognition. IEEE transactions on pattern analysis and machine intelligence 32 (9): 1582–1596, 2010.
van de Sande, K. E., Gevers, T., and Snoek, C. G. Evaluation of color descriptors for object and scene recognition. In IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, USA (June 2008), 2004.
Zagoris, K., Chatzichristofis, S. A., Papamarkos, N., and Boutalis, Y. S. Automatic image annotation and retrieval using the joint composite descriptor. In Informatics (PCI), 2010 14th Panhellenic Conference on. IEEE, pp. 143–147, 2010.
Bosch, A., Zisserman, A., and Munoz, X. Representing shape with a spatial pyramid kernel. In Proceedings of the 6th ACM International Conference on Image and Video Retrieval. ACM, pp. 401–408, 2007.
Carpenter, J. May the best analyst win. Science 331 (6018): 698–699, 2011.
Chatzichristofis, S. A. and Boutalis, Y. S. Cedd: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. In Int. Conf. on Computer Vision Systems. Springer, pp. 312–322, 2008a.
Chatzichristofis, S. A. and Boutalis, Y. S. Fcth: Fuzzy color and texture histogram-a low level feature for accurate image retrieval. In Image Analysis for Multimedia Interactive Services, WIAMIS’08. IEEE, pp. 191–196, 2008b.
Chodas, P. Overview of the jpl center for neo studies (cneos). In AAS/Division for Planetary Sciences Meeting Abstracts. Vol. 47, 2015.
Fernández, A., García, S., del Jesus, M. J., and Herrera, F. A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets. Fuzzy Sets and Systems 159 (18): 2378–2398, 2008.
Fogel, I. and Sagi, D. Gabor filters as texture discriminator. Biological cybernetics 61 (2): 103–113, 1989.
Guyon, I., Cawley, G., Dror, G., and Saffari, A. Hands-on pattern recognition challenges in machine learning, volume, 2011.
Han, J. and Ma, K.-K. Fuzzy color histogram and its use in color image retrieval. IEEE transactions on image processing 11 (8): 944–952, 2002.
Haralick, R. M., Shanmugam, K., et al. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics (6): 610–621, 1973.
Huang, J., Kumar, S. R., Mitra, M., Zhu, W.-J., and Zabih, R. Image indexing using color correlograms. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp. 762–768, 1997.
Jenniskens, P. Meteor showers in review. Planetary and Space Science vol. 143, pp. 116–124, 2017.
Kriegel, H.-P., Schubert, E., and Zimek, A. Evaluation of multiple clustering solutions. In MultiClust@ ECML/PKDD. pp. 55–66, 2011.
Milano-Oliveira, L. F. and Kaster, D. Defining similarity spaces for large-scale image retrieval through scientific workflows. In Proceedings of the 21st Int. Database Engineering & Applications Symposium. ACM, pp. 57–65, 2017.
Novak, C. L. and Shafer, S. A. Anatomy of a color histogram. In Computer Vision and Pattern Recognition, 1992. Proceedings CVPR’92., 1992 IEEE Computer Society Conference on. IEEE, pp. 599–605, 1992.
Ojala, T., Pietikainen, M., and Harwood, D. Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In Proceedings of the 12th IAPR International Conference on Pattern Recognition. Vol. 1. IEEE, pp. 582–585, 1994.
Rosset, S. and Inger, A. Kdd-cup 99: knowledge discovery in a charitable organization’s donor database. SIGKDD Explorations 1 (2): 85–90, 2000.
Scott, D. W. Averaged shifted histogram. Wiley Interdisciplinary Reviews: Computational Statistics 2 (2): 160–164, 2010.
Sikora, T. The mpeg-7 visual standard for content description-an overview. IEEE Transactions on circuits and systems for video technology 11 (6): 696–702, 2001.
Tamura, H., Mori, S., and Yamawaki, T. Textural features corresponding to visual perception. IEEE Transactions on Systems, man, and cybernetics 8 (6): 460–473, 1978.
Taylor, S. and Drummond, T. Binary histogrammed intensity patches for efficient and robust matching. International journal of computer vision 94 (2): 241–265, 2011.
Van De Sande, K., Gevers, T., and Snoek, C. Evaluating color descriptors for object and scene recognition. IEEE transactions on pattern analysis and machine intelligence 32 (9): 1582–1596, 2010.
van de Sande, K. E., Gevers, T., and Snoek, C. G. Evaluation of color descriptors for object and scene recognition. In IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, USA (June 2008), 2004.
Zagoris, K., Chatzichristofis, S. A., Papamarkos, N., and Boutalis, Y. S. Automatic image annotation and retrieval using the joint composite descriptor. In Informatics (PCI), 2010 14th Panhellenic Conference on. IEEE, pp. 143–147, 2010.
Publicado
22/10/2018
Como Citar
LORENA, A. C.; KASTER, D. S.; CERRI, R.; FARIA, E. R.; MELO, V. V. de.
Can I make a wish?: a competition on detecting meteors in images. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 6. , 2018, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 89-96.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2018.27389.
