BEHAVIOR OF DIFFERENT IMAGE CLASSIFIERS WITHIN A BROAD DOMAIN
B. Clemente, M. L. Durán, A. Caro, P. G. Rodríguez
2009
Abstract
Image classification is one of the most important research tasks in the Content-Based Image Retrieval area. The term image categorization refers to the labeling of the images under one of a number of predefined categories. Although this task is usually not too difficult for humans, it has proved to be extremely complex for machines (or computer programs). The major issues concern variable and sometimes uncontrolled imaging conditions. This paper focuses on observation of behavior for different classifiers within a collection of general purpose images (photos). We carry out a contrastive study between the groups obtained from these mathematical classifiers and a prior classification developed by humans.
References
- Caro, A., Alonso, T., Rodríguez, P., Durán, M., and Í vila, M. (2007a). Testing geodesic active contours. LNCS 4478:64-71.
- Caro, A., Rodríguez, P., Antequera, T., and Palacios, R. (2007b). Feasible application of shape-based classification. LNCS 4477:588-595.
- Chang, C. and Lin, C. (2001). LIBSVM: a Library for Support Vector Machines. Department of Computer Science and Information Engineering, National Taiwan University, http://www.csie.ntu.edu.tw/ cjlin/libsvm.
- Chu, A. (1990). Use of grey value distribution of run lengths for texture analysis. Pattern Recognition Letters, 11:415-420.
- Cinque, L., Levialdi, S., Pellicano, A., and Olsen, K. (1999). Color-based image retrieval using spatialchromatic histograms. In IEEE. International Conference on Multimedia Computing and Systems, volume 2, pages 969-973.
- Cristianini, N. and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and other kernel-based learning methods. Uiversity Press, Cambridge.
- Domingos, P. and Pazzani, M. (1996). Beyond independence: conditions for the optimality of the simple bayesian classifier. In Machine Learning: Proceedings of the Thirteenth International Conference, pages 105-112. Morgan Kaufmann.
- Dumais, S., Platt, J., Heckerman, D., and Sahami, M. (1998). Inductive learning algorithms and representations for text categorization. In Proc. of 7th International conference on Information and Knowledge Management.
- El-Naqa, I., Yongyi, Y., Galatsanos, N., Nishikawa, R., and Wernick, M. (2004). A similarity learning approach to content-based image retrieval: application to digital mammography. In IEEE Transactions on Medical Imaging, volume 23, pages 1233-1244.
- Fernández, M., Carrión, P., Cernadas, E., and Gálvez, J. (2003). Improved classification of pollen texture images using svm and mlp. In 3rd IASTED international conference on visualization, imaging and image processing.
- Forczmanski, P. and Frejlichowski, D. (2008). Computer Recognition Systems 2, volume 45 of Advances in Soft Computing, chapter Strategies of Shape and Color Fusions for Content Based Image Retrieval, pages 3-10. Springer Berlin / Heidelberg.
- Galloway, M. (1975). Texture analysis using grey level run lengths. Computer Graphics and Imag. Processing, 4:172-179.
- Haralick, R. and Shapiro, L. (1993). Computer and Robot Vision. Addison-Wesley.
- Li, J. and Wang, J. (2005). Alip: the automatic linguistic indexing of pictures system. Computer Vision and Pattern Recognition, 2:1208-1209.
- MacDonald, L. and Luo, M. (2002). Colour Image Science: Exploiting Digital Media. Wiley.
- Mehtre, B., Kankanhalli, M., and Lee, W. (1998). Contentbased image retrieval using a composite color-shape approach. Information Processing and Management, 34(1):109-120.
- Saber, E., Tekalp, A. M., Eschbach, R., and Knox, K. (1996). Automatic image annotation using adaptive color classification. Graph. Models Image Process., 58(2):115-126.
- 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 Trans. Pattern Anal. Mach. Intell., 22(12):1349-1380.
- Vailaya, A., Figueiredo, M., Jain, A., and Hong Jiang, Z. (1999). Content-based hierarchical classification of vacation images. In Multimedia Computing and Systems, IEEE International Conference, pages 518-523.
Paper Citation
in Harvard Style
Clemente B., Durán M., Caro A. and Rodríguez P. (2009). BEHAVIOR OF DIFFERENT IMAGE CLASSIFIERS WITHIN A BROAD DOMAIN . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, pages 278-283. DOI: 10.5220/0002297002780283
in Bibtex Style
@conference{kdir09,
author={B. Clemente and M. L. Durán and A. Caro and P. G. Rodríguez},
title={BEHAVIOR OF DIFFERENT IMAGE CLASSIFIERS WITHIN A BROAD DOMAIN},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)},
year={2009},
pages={278-283},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002297002780283},
isbn={978-989-674-011-5},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)
TI - BEHAVIOR OF DIFFERENT IMAGE CLASSIFIERS WITHIN A BROAD DOMAIN
SN - 978-989-674-011-5
AU - Clemente B.
AU - Durán M.
AU - Caro A.
AU - Rodríguez P.
PY - 2009
SP - 278
EP - 283
DO - 10.5220/0002297002780283