Actas de congresos
Classifier Selection Based On The Correlation Of Diversity Measures: When Fewer Is More
Brazilian Symposium Of Computer Graphic And Image Processing. , v. , n. , p. 16 - 23, 2013.
Dos Santos J.A.
The ever-growing access to high-resolution images has prompted the development of region-based classification methods for remote sensing images. However, in agricultural applications, the recognition of specific regions is still a challenge as there could be many different spectral patterns in a same studied area. In this context, depending on the features used, different learning methods can be used to create complementary classifiers. Many researchers have developed solutions based on the use of machine learning techniques to address these problems. Examples of successful initiatives are those dedicated to the development of learning techniques for data fusion or Multiple Classifier Systems (MCS). In MCS, diversity becomes an essential factor for their success. Different works have been using diversity measures to select appropriate high-performance classifiers, but the challenge of finding the optimal number of classifiers for a target task has not been properly addressed yet. In general, the proposed solutions rely on the a priori use of ad hoc strategies for selecting classifiers, followed by the evaluation of their effectiveness results during training. Searching by the optimal number of classifiers, however, makes the selection process more expensive. In this paper, we address this issue by proposing a novel strategy for selecting classifiers to be combined based on the correlation of different diversity measures. Diversity measures are used to rank pairs of classifiers and the agreement among ranked lists guides the classifier selection process. A fusion framework has been used in our experiments targeted to the classification of coffee crops in remote sensing images. Experiment results demonstrate that the novel strategy is able to yield comparable effectiveness results when contrasted to several baselines, but using much fewer classifiers. © 2013 IEEE.1623Microsoft ResearchCastillejo-González, López-Granados, García-Ferrer, Peña-Barragán, Jurado-Expósito, De La Orden, González-Audicana, Object-and pixel-based analysis for mapping crops and their agro-environmental associated measures using quickbird imagery (2009) Elsevier Computer and Electronics in AgricultureDos Santos, J.A., Faria, F.A., Calumby, R., Da S Torres, R., Lamparelli, R., A genetic programming approach for coffee crop recognition (2010) IEEE Geoscience and Remote Sensing SymposiumPouteau, R., Stoll, B., Svm selective fusion (self) for multi-source classification of structurally complex tropical rainforest (2012) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5 (4), pp. 1203-1212. , augDuro, D.C., Franklin, S.E., Dubé, M.G., A comparison of pixelbased and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using spot-5 hrg imagery (2012) Remote Sensing of Environment, 118, pp. 259-272Blaschke, T., Object based image analysis for remote sensing (2010) ISPRS Journal of Photogrammetry and Remote SensingDos Santos, J.A., Penatti, O.A.B., Da S Torres, R., Evaluating the potential of texture and color descriptors for remote sensing image retrieval and classification (2010) Intl. Conf. on Computer Vision Theory and Applications, pp. 203-208. , Angers, France, MayRocha, A., Papa, J., Meira, L., How far you can get using machine learning black-boxes (2010) Conf. on Graphics, Patterns and Images, pp. 193-200. , 30 2010-sept. 3Lu, D., Weng, Q., A survey of image classification methods and techniques for improving classification performance (2007) Intl. Journal of Remote Sensing, 28 (5), pp. 823-870Mountrakis, G., Im, J., Ogole, C., Support vector machines in remote sensing: A review (2011) ISPRS Journal of Photogrammetry and Remote Sensing, 66 (3), pp. 247-259Faria, F.A., Dos Santos, J.A., Torres, R.D.S., Rocha, A., Falcão, A.X., Automatic fusion of region-based classifiers for coffee crop recognition (2012) IEEE Geoscience and Remote Sensing SymposiumKuncheva, L.I., Whitaker, C.J., Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy (2003) Machine LearningShipp, C.A., Kuncheva, L.I., An investigation into how adaboost affects classifier diversity (2009) IPMU, pp. 203-208Faria, F.A., Santos, J.A., Rocha, A., Torres, R.D.S., Automatic classifier fusion for produce recognition (2012) Conf. on Graphics, Patterns and ImagesDos Santos, J.A., Faria, F.A., Torres, R.D.S., Rocha, A., Gosselin, P.-H., Philipp-Foliguet, S., Falcao, A., Descriptor correlation analysis for remote sensing image multi-scale classification (2012) Intl. Conf. on Pattern Recognition, pp. 3078-3081Du, P., Xia, J., Zhang, W., Tan, K., Liu, Y., Liu, S., Multiple classifier system for remote sensing image classification: A review (2012) Sensors, 12 (4), p. 4764Boser, B.E., Guyon, I.M., Vapnik, V.N., A training algorithm for optimal margin classifiers (1992) Workshop on Computational Learning Theory, Ser. COLT '92, pp. 144-152Cristianini, N., Shawe-Taylor, J., (2000) An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, , Cambridge University PressGuigues, L., Cocquerez, J., Le Men, H., Scale-sets image analysis (2006) Intl. Journal of Computer VisionMyneni, R.B., Hall, F.G., Sellers, P.J., Marshak, A.L., The interpretation of spectral vegetation indexes (1995) Geoscience and Remote Sensing, IEEE Transactions on, 33 (2), pp. 481-486Stehling, R., Nascimento, M., Falcao, A., A compact and efficient image retrieval approach based on border/interior pixel classification (2002) ACM Conf. on Information and Knowledge Management, pp. 102-109Pass, G., Zabih, R., Miller, J., Comparing images using color coherence vectors (1996) ACM Intl. Conf. on Multimedia, pp. 65-73Swain, M., Ballard, D., Color indexing (1991) Intl. Journal of Computer Vision, 7 (1), pp. 11-32Huang, C., Liu, Q., An orientation independent texture descriptor for image retrieval (2007) Intl. Conf. on Communications, Circuits and Systems, pp. 772-776Zegarra, J., Leite, N., Torres, R., Wavelet-based feature extraction for fingerprint image retrieval (2008) Journal of Computational and Applied Mathematics, 227 (2), pp. 294-307Unser, M., Sum and difference histograms for texture classification (1986) IEEE Transactions Pattern Analysis and Machine Intelligence, 8 (1), pp. 118-125Penatti, O.A.B., Valle, E., Torres, R.D.S., Comparative study of global color and texture descriptors for web image retrieval (2012) Journal of Visual Communication and Image RepresentationBrennan, R.L., Prediger, D.J., Coefficient kappa: Some uses, misuses, and alternatives (1981) Educational and Psychological MeasurementMa, Z., Redmond, R.L., Tau coefficients for accuracy assessment of classification of remote sensing data (1995) Photogrametric Engineering and Remote SensingKendall, M.G., A new measure of rank correlation (1938) Biometrika, 30 (1-2), pp. 81-93. , Jun