Swarm-based Descriptor Combination and its Application for Image Classification

In this paper, we deal with the descriptor combination problem in image classification tasks. This problem refers to the definition of an appropriate combination of image content descriptors that characterize different visual properties, such as color, shape and texture. In this paper, we propose to model the descriptor combination as a swarm-based optimization problem, which finds out the set of parameters that maximizes the classification accuracy of the Optimum-Path Forest (OPF) classifier. In our model, a descriptor is seen as a pair composed of a feature extraction algorithm and a suitable distance function. Our strategy here is to combine distance scores defined by different descriptors, as well as to employ them to weight OPF edges, which connect samples in the feature space. An extensive evaluation of several swarm-based optimization techniques was performed. Experimental results have demonstrated the robustness of the proposed combination approach

A Model of Animal Spirits via Sentiment Spreading

In order to incorporate animal spirits in a scientifically rigorous inquiry about the causes of aggregate business cycles, one needs to explore the foundations of human behavior, namely concerning the process through which sentiment switching occurs. Which factors drive human sentiments? In what conditions a pessimistic individual becomes an optimist, or the other way around? Is it possible to justify persistent waves of optimism and pessimism under reasonable assumptions concerning social behavior? This article proposes a framework to address the posed questions. The setup is based on rumor propagation theory and it explains how social interaction may lead individuals to change from one sentiment state to the other, eventually triggering a rotation between periods of dominant optimism and periods of dominant pessimism.info:eu-repo/semantics/publishedVersio

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

A new parallel training algorithm for optimum-path forest-based learning

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.Trabajo de investigació

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Although nontechnical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy and to characterize possible illegal consumers has not attracted much attention in this context. In this paper, we focus on this problem by reviewing three evolutionary-based techniques for feature selection, and we also introduce one of them in this context. The results demonstrated that selecting the most representative features can improve a lot of the classification accuracy of possible frauds in datasets composed by industrial and commercial profiles.271140146Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Learning how to extract rotation-invariant and scale-invariant features from texture images

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.Trabajo de investigació

Novel approaches for exclusive and continuous fingerprint classification

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.Trabajo de investigació

Rotation-invariant texture recognition

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.Trabajo de investigació