OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification

Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.

A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. © 2013 Elsevier Ltd. All rights reserved.

Explicacion breve del analisis de trayectoria (Path analysis) con un ejemplo que emplea un programa disponible en el terminal APL de CELADE

Incluye Bibliografía

Report of the seminar on Caribbean development thinking the path covered and the way forward

The Economic Commission for Latin America and the Caribbean (ECLAC), Subregional Headquarters for the Caribbean, and secretariat of the Caribbean Development and Cooperation Committee (CDCC) convened a Seminar on Caribbean Development Thinking: The Path Covered and the Way Forward, in Port of Spain on 21 October 2009. The meeting was attended by representatives of the following CDCC member countries: Dominica, Grenada, Guyana, Jamaica, Saint Lucia, Saint Kitts and Nevis, Saint Vincent and the Grenadines, Suriname and Trinidad and Tobago. Representatives of the following organizations of the United Nations system also attended: the International Labour Organisation (ILO); Joint United Nations Project on HIV/AIDS (UNAIDS); and the United Nations Development Programme (UNDP). The following intergovernmental organizations were represented: the Caribbean Community (CARICOM); the Caribbean Development Bank (CDB); the Caribbean Regional Negotiating Mechanism/Caribbean Community (CRNM/CARICOM); Delegation of the European Commission in Trinidad and Tobago; the Organisation of American States (OAS); and the Organisation of Eastern Caribbean States (OECS). The University of the West Indies (UWI) also participated. The list of participants appears as annex I to this report.

PATH dependency e a transformação agrária do bioma amazônico: o sentido econômico das capoeiras para o desenvolvimento sustentável

As capoeiras - áreas alteradas por ação antrópica que se encontram em estágios de regeneração espontânea de cobertura florestal - são componentes da paisagem rural de grande significado na Amazônia. No último Censo Agropecuário, as áreas de capoeira perfaziam 4,5 milhões de hectares em toda a Região Norte. Uma literatura crescentemente importante considera tais áreas proxy de economias rurais decadentes e insustentáveis, sobre as quais se ergue uma pecuária de corte eficiente e sustentável. Este artigo procura estabelecer os diferentes tipos de capoeira que se constatam na economia rural da Amazônia, associando-as às diferentes formas de produção, cujos sistemas se expressam dinamicamente como trajetórias tecnológicas concorrentes. A partir daí a) demonstra que parte dessas áreas resulta de mudanças positivas nos sistemas produtivos que produzem capoeiras com grande capacidade de regeneração – estando associada, portanto, a inovações relevantes para o desenvolvimento da Região numa perspectiva que incorpora critérios de sustentabilidade ambiental; b) demonstra que os tipos de capoeira que indicam degradação, pela baixa capacidade de regeneração, se associam à pecuária de corte, a qual na Região tem apresentado dificuldades estruturais de modernização técnica e c) indica que o ambiente institucional, favorecendo os sistemas que produzem capoeira degradada em detrimento daqueles que produzem capoeiras de rápida recomposição, podem aprisionar (levar a um lock-in) a economia agrária da região nas piores soluções, tanto econômica, quanto social e ecologicamente.

EEG signal classification for epilepsy diagnosis via optimum path forest - A systematic assessment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A path- and label-cost propagation approach to speedup the training of the optimum-path forest classifier

In general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies that make effort to employ machine learning algorithms in the context of big data classification problems. The research on this area ranges from Graphics Processing Units-based implementations to mathematical optimizations, being the main drawback of the former approaches to be dependent on the graphic video card. Here, we propose an architecture-independent optimization approach for the optimum-path forest (OPF) classifier, that is designed using a theoretical formulation that relates the minimum spanning tree with the minimum spanning forest generated by the OPF over the training dataset. The experiments have shown that the approach proposed can be faster than the traditional one in five public datasets, being also as accurate as the original OPF. (C) 2014 Elsevier B. V. All rights reserved.

A local-global scheme for tracking crack path in three-dimensional solids

This paper aims to contribute to the three-dimensional generalization of numerical prediction of crack propagation through the formulation of finite elements with embedded discontinuities. The analysis of crack propagation in two-dimensional problems yields lines of discontinuity that can be tracked in a relatively simple way through the sequential construction of straight line segments oriented according to the direction of failure within each finite element in the solid. In three-dimensional analysis, the construction of the discontinuity path is more complex because it requires the creation of plane surfaces within each element, which must be continuous between the elements. In the method proposed by Chaves (2003) the crack is determined by solving a problem analogous to the heat conduction problem, established from local failure orientations, based on the stress state of the mechanical problem. To minimize the computational effort, in this paper a new strategy is proposed whereby the analysis for tracking the discontinuity path is restricted to the domain formed by some elements near the crack surface that develops along the loading process. The proposed methodology is validated by performing three-dimensional analyses of basic problems of experimental fractures and comparing their results with those reported in the literature.

Static Video Summarization through Optimum-Path Forest Clustering

This paper introduces the Optimum-Path Forest (OPF) classifier for static video summarization, being its results comparable to the ones obtained by some state-of-the-art video summarization techniques. The experimental section has been conducted using several image descriptors in two public datasets, followed by an analysis of OPF robustness regarding one ad-hoc parameter. Future works are guided to improve OPF effectiveness on each distinct video category.

Optimum-Path Forest Applied for Breast Masses Classification

In Computer-Aided Diagnosis-based schemes in mammography analysis each module is interconnected, which directly affects the system operation as a whole. The identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest for further image segmentation. This study aims to evaluate the performance of three techniques in classifying regions of interest as containing masses or without masses (without clinical findings), as well as the main contribution of this work is to introduce the Optimum-Path Forest (OPF) classifier in this context, which has never been done so far. Thus, we have compared OPF against with two sorts of neural networks in a private dataset composed by 120 images: Radial Basis Function and Multilayer Perceptron (MLP). Texture features have been used for such purpose, and the experiments have demonstrated that MLP networks have been slightly better than OPF, but the former is much faster, which can be a suitable tool for real-time recognition systems.

Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Automatic visual dictionary generation through optimum-path forest clusteringautomatic visual dictionary generation through optimum-path forest clustering

Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation.

A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A simple geometrical path towards hybrid orbitals

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)