Browsing by Author "Paulo Papa, Joao"

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    A new parallel training algorithm for optimum-path forest-based learning
    (Springer Verlag, 2017) Culquicondor, Aldo; Castelo Fernández, Cesar; Paulo Papa, Joao
    In this work, we present a new parallel-driven approach to speed up Optimum-Path Forest (OPF) training phase. In addition, we show how to make OPF up to five times faster for training using a simple parallel-friendly data structure, which can achieve the same accuracy results to the ones obtained by traditional OPF. To the best of our knowledge, we have not observed any work that attempted at parallelizing OPF to date, which turns out to be the main contribution of this paper. The experiments are carried out in four public datasets, showing the proposed approach maintains the trade-off between efficiency and effectiveness. © Springer International Publishing AG 2017.
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    Learning how to extract rotation-invariant and scale-invariant features from texture images
    (Scopus, 2008) Montoya Zegarra, Javier; Paulo Papa, Joao; Leite, Neucimar; da Silva Torres, Ricardo; Falcao, Alexandre
    Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system.
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    Novel approaches for exclusive and continuous fingerprint classification
    (Scopus, 2009) Montoya Zegarra, Javier; Paulo Papa, Joao; Leite, Neucimar; da Silva Torres, Ricardo; Falcao, Alexandre
    This paper proposes novel exclusive and continuous approaches to guide the search and the retrieval in fingerprint image databases. Both approaches are useful to perform a coarse level classification of fingerprint images before fingerprint authentication tasks. Our approaches are characterized by: (1) texture image descriptors based on pairs of multi-resolution decomposition methods that encode effectively global and local fingerprint information, with similarity measures used for fingerprint matching purposes, and (2) a novel multi-class object recognition method based on the Optimum Path Forest classifier. Experiments were carried out on the standard NIST-4 dataset aiming to study the discriminative and scalability capabilities of our approaches. The high classification rates allow us demonstrate the feasibility and validity of our approaches for characterizing fingerprint images accurately. © 2009 Springer Berlin Heidelberg.
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    Rotation-invariant texture recognition
    (Scopus, 2007) Montoya Zegarra, Javier; Paulo Papa, Joao; Leite, Neucimar; da Silva Torres, Ricardo; Falcao, Alexandre
    This paper proposes a new texture classification system, which is distinguished by: (1) a new rotation-invariant image descriptor based on Steerable Pyramid Decomposition, and (2) by a novel multi-class recognition method based on Optimum Path Forest. By combining the discriminating power of our image descriptor and classifier, our system uses small size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz dataset. High classification rates demonstrate the superiority of the proposed method. © Springer-Verlag Berlin Heidelberg 2007.