Um arcabouço para seleção e fusão de classificadores de padrão

Orientadores: Ricardo da Silva Torres, Anderson RochaTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O crescente aumento de dados visuais, seja pelo uso de inúmeras câmeras de vídeo monitoramento disponíveis ou pela popularização de dispositivos móveis que permitem pessoas criar, editar e compartilhar suas próprias imagens/vídeos, tem contribuído enormemente para a chamada ''big data revolution". Esta grande quantidade de dados visuais dá origem a uma caixa de Pandora de novos problemas de classificação visuais nunca antes imaginados. Tarefas de classificação de imagens e vídeos foram inseridos em diferentes e complexas aplicações e o uso de soluções baseadas em aprendizagem de máquina tornou-se mais popular para diversas aplicações. Entretanto, por outro lado, não existe uma ''bala de prata" que resolva todos os problemas, ou seja, não é possível caracterizar todas as imagens de diferentes domínios com o mesmo método de descrição e nem utilizar o mesmo método de aprendizagem para alcançar bons resultados em qualquer tipo de aplicação. Nesta tese, propomos um arcabouço para seleção e fusão de classificadores. Nosso método busca combinar métodos de caracterização de imagem e aprendizagem por meio de uma abordagem meta-aprendizagem que avalia quais métodos contribuem melhor para solução de um determinado problema. O arcabouço utiliza três diferentes estratégias de seleção de classificadores para apontar o menos correlacionados e eficazes, por meio de análises de medidas de diversidade. Os experimentos mostram que as abordagens propostas produzem resultados comparáveis aos famosos métodos da literatura para diferentes aplicações, utilizando menos classificadores e não sofrendo com problemas que afetam outras técnicas como a maldição da dimensionalidade e normalização. Além disso, a nossa abordagem é capaz de alcançar resultados eficazes de classificação usando conjuntos de treinamento muito reduzidosAbstract: The frequent growth of visual data, either by countless available monitoring video cameras or the popularization of mobile devices that allow each person to create, edit, and share their own images and videos have contributed enormously to the so called ''big-data revolution''. This shear amount of visual data gives rise to a Pandora box of new visual classification problems never imagined before. Image and video classification tasks have been inserted in different and complex applications and the use of machine learning-based solutions has become the most popular approach to several applications. Notwithstanding, there is no silver bullet that solves all the problems, i.e., it is not possible to characterize all images of different domains with the same description method nor is it possible to use the same learning method to achieve good results in any kind of application. In this thesis, we aim at proposing a framework for classifier selection and fusion. Our method seeks to combine image characterization and learning methods by means of a meta-learning approach responsible for assessing which methods contribute more towards the solution of a given problem. The framework uses three different strategies of classifier selection which pinpoints the less correlated, yet effective, classifiers through a series of diversity measure analysis. The experiments show that the proposed approaches yield comparable results to well-known algorithms from the literature on many different applications but using less learning and description methods as well as not incurring in the curse of dimensionality and normalization problems common to some fusion techniques. Furthermore, our approach is able to achieve effective classification results using very reduced training setsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Discovering relevant spatial filterbanks for VHR image classification

International audienc

Discovering relevant spatial filterbanks for VHR image classification

In very high resolution (VHR) image classification it is common to use spatial filters to enhance the discrimination among landuses related to similar spectral properties but different spatial characteristics. However, the filters types are numerous (e.g. textural, morphological, Gabor, wavelets, etc.) and the user must pre-select a family of features, as well as their specific parameters. This results in features spaces that are high dimensional and redundant and thus require long and suboptimal feature selection phases. We propose to discover the relevant filters as well as their parameters with a sparsity promoting regularization and an active set algorithm that iteratively adds to the model the most promising features. This way, we explore the filters/parameters input space efficiently (which is infinitely large for continuous parameters) and construct the optimal filterbank for classification without any other information than the types of filters to be used.

Discovering relevant spatial filterbanks for VHR image classification

International audienc

Discovering relevant spatial filterbanks for VHR image classification

In very high resolution (VHR) image classification it is common to use spatial filters to enhance the discrimination among landuses related to similar spectral properties but different spatial characteristics. However, the filters types that can be used are numerous (e.g. textural, morphological, Gabor, wavelets, etc.) and the user must pre-select a family of features, as well as their specific parameters. This results in features spaces that are high dimensional and redundant, thus requiring long and suboptimal feature selection phases. In this paper, we propose to discover the relevant filters as well as their parameters with a sparsity promoting regular-ization and an active set algorithm that iteratively adds to the model the most promising features. This way, we explore the filters/parameters input space efficiently (which is infinitely large for continuous parameters) and construct the optimal filterbank for classification without any other information than the types of filters to be used