Abstract
Nowadays a huge volume of data (e.g. images and videos) are daily generated in several areas. The importance of this subject has led to a new paradigm known as eScience. In this scenario, the biological image domain emerges as an important research area given the great impact that it can leads in real solutions and people’s lives. On the other hand, to cope with this massive data it is necessary to integrate into the same environment not only several techniques involving image processing, description and classification, but also feature selection methods. Hence, in the present paper we propose a new framework capable to join these techniques in a single and efficient pipeline, in order to characterize biological images. Experiments, performed with the ImageCLEF dataset, have shown that the proposed framework presented notable results, reaching up to 87.5% of accuracy regarding the plant species classification, which is highly relevant and a non-trivial task.
This work has been supported by Fundação Araucária and CNPq.
Chapter PDF
Similar content being viewed by others
References
Gray, J.: Jim gray on escience: a transformed scientific method. The Fourth Paradigm: Data-intensive Scientific Discovery (2009)
Gantz, J., Reinsel, D.: Extracting value from chaos. IDC iView, 1–12 (2011)
Peng, H.: Bioimage informatics: a new area of engineering biology. Bioinformatics 24(17), 1827–1836 (2008)
Müller, H., Clough, P., Deselaers, T., Caputo, B.: ImageCLEF: Experimental Evaluation in Visual Information Retrieval, vol. 32. Springer (2010)
Bartolini, I., Ciaccia, P., Patella, M.: Warp: Accurate retrieval of shapes using phase of fourier descriptors and time warping distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(1), 142–147 (2005)
da Fontoura Costa, L., Cesar Jr., R.M.: Shape analysis and classification: theory and practice, 2nd edn. CRC Press (2010)
Attig, A., Perner, P.: A comparison between haralick’s texture descriptor and the texture descriptor based on random sets for biological images. In: Perner, P. (ed.) MLDM 2011. LNCS, vol. 6871, pp. 524–538. Springer, Heidelberg (2011)
Huang, C.B., Liu, Q.: An orientation independent texture descriptor for image retrieval. In: Int. Conf. on Communic., Circ. and Systems, pp. 772–776. IEEE (2007)
Han, J., Kamber, M.: Data mining: concepts and techniques. Morgan Kaufmann (2006)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6(1), 37–66 (1991)
Lewis, D.D.: Naive (bayes) at forty: The independence assumption in information retrieval. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 4–15. Springer, Heidelberg (1998)
Gardner, M., Dorling, S.: Artificial neural networks–a review of applications in the atmospheric sciences. Atmospheric Environment 32(14-15), 2627–2636 (1998)
Statistics, L.B., Breiman, L.: Random forests. Machine Learning, 5–32 (2001)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)
Abe, S.: Support vector machines for pattern classification. Springer (2010)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)
Mucciardi, A.N., Gose, E.E.: A comparison of seven techniques for choosing subsets of pattern recognition properties. IEEE Trans. on Comp. 100(9), 1023–1031 (1971)
Lopes, F.M., Martins Jr., D.C., Cesar Jr., R.M.: Feature selection environment for genomic applications. BMC Bioinformatics 9(1), 451 (2008)
Lopes, F.M., de Oliveira, E.A., Cesar Jr., R.M.: Analysis of the GRNs inference by using Tsallis entropy and a feature selection approach. In: Bayro-Corrochano, E., Eklundh, J.-O. (eds.) CIARP 2009. LNCS, vol. 5856, pp. 473–480. Springer, Heidelberg (2009)
Lopes, F.M., Martins Jr., D.C., Barrera, J., Cesar Jr., R.M.: SFFS-MR: A floating search strategy for GRNs inference. In: Dijkstra, T.M.H., Tsivtsivadze, E., Marchiori, E., Heskes, T. (eds.) PRIB 2010. LNCS, vol. 6282, pp. 407–418. Springer, Heidelberg (2010)
Pinto, S.C.D., Mena-Chalco, J.P., Lopes, F.M., Velho, L., Cesar Jr., R.M.: 3D facial expression analysis by using 2D and 3D wavelet transforms. In: ICIP, pp. 1281–1284 (2011)
John, G.H., Kohavi, R., Pfleger, K., et al.: Irrelevant features and the subset selection problem. In: 11th Int. Conf. on Machine Learning, pp. 121–129 (1994)
Hall, M.A.: Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato (1999)
Sahoo, P.K., Soltani, S., Wong, A.: A survey of thresholding techniques. Computer Vision, Graphics, and Image Processing 41(2), 233–260 (1988)
Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: 23rd International Conference on Machine Learning, pp. 233–240. ACM (2006)
Goëau, H., Bonnet, P., Joly, A., Yahiaoui, I., Barthélémy, D., Boujemaa, N., Molino, J.: The ImageCLEF 2012 Plant Identification Task (2012)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Brilhador, A., Colonhezi, T.P., Bugatti, P.H., Lopes, F.M. (2013). Combining Texture and Shape Descriptors for Bioimages Classification: A Case of Study in ImageCLEF Dataset. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41822-8_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-41822-8_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41821-1
Online ISBN: 978-3-642-41822-8
eBook Packages: Computer ScienceComputer Science (R0)