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Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation

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Abstract

Multiple classifiers on the dissimilarity space are proposed to address the problem of forest species recognition from microscopic images. To that end, classical texture-based features such as Gabor filters, local binary patterns (LBP) and local phase quantization (LPQ), as well as two keypoint-based features, the scale-invariant feature transform (SIFT) and the speeded up robust features (SURF), are used to generate a pool of diverse classifiers on the dissimilarity space. A comprehensive set of experiments on a database composed of 2,240 microscopic images from 112 different forest species was used to evaluate the performance of each individual classifier of the generated pool, the combination of all classifiers, and different dynamic selection of classifiers (DSC) methods. The best result (93.03 %) was observed by incorporating probabilistic information in a DSC method based on multiple classifier behavior.

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Acknowledgments

This research has been supported by The National Council for Scientific and Technological Development (CNPq) grants 301653/2011-9 and 471050/2013-0 and the Coordination for the Improvement of Higher Education Personnel (CAPES).

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Authors and Affiliations

  1. Federal University of Parana (UFPR), R. Rua Cel. Francisco H. dos Santos, 100, Curitiba, PR, 81531-990, Brazil

    J. G. Martins & L. S. Oliveira

  2. Pontifical Catholic University of Parana (PUCPR), R. Imaculada Conceição, 1155, Curitiba, PR, 80215-901, Brazil

    A. S. Britto Jr.

  3. Ecole de Technologie Superieure, 1100 rue Notre Dame Ouest, Montreal, QC, Canada

    R. Sabourin

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  1. J. G. Martins
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  2. L. S. Oliveira
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Correspondence to L. S. Oliveira.

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Martins, J.G., Oliveira, L.S., Britto, A.S. et al. Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation. Machine Vision and Applications 26, 279–293 (2015). https://doi.org/10.1007/s00138-015-0659-0

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