Gray-level Texture Characterization Based on a New Adaptive

In this paper, we propose a new nonlinear exponential adaptive two-dimensional (2-D) filter for texture characterization. The filter coefficients are updated with the Least Mean Square (LMS) algorithm. The proposed nonlinear model is used for texture characterization with a 2-D Auto-Regressive (AR) adaptive model. The main advantage of the new nonlinear exponential adaptive 2-D filter is the reduced number of coefficients used to characterize the nonlinear parametric models of images regarding the 2-D second-order Volterra model. Whatever the degree of the non-linearity, the problem results in the same number of coefficients as in the linear case. The characterization efficiency of the proposed exponential model is compared to the one provided by both 2-D linear and Volterra filters and the cooccurrence matrix method. The comparison is based on two criteria usually used to evaluate the features discriminating ability and the class quantification. Extensive experiments proved that the exponential model coefficients give better results in texture discrimination than several other parametric features even in a noisy context

Gray-level Texture Characterization Based on a New Adaptive Nonlinear Auto-Regressive Filter

In this paper, we propose a new nonlinear exponential adaptive two-dimensional (2-D) filter for texture characterization. The filter coefficients are updated with the Least Mean Square (LMS) algorithm. The proposed nonlinear model is used for texture characterization with a 2-D Auto-Regressive (AR) adaptive model. The main advantage of the new nonlinear exponential adaptive 2-D filter is the reduced number of coefficients used to characterize the nonlinear parametric models of images regarding the 2-D second-order Volterra model. Whatever the degree of the non-linearity, the problem results in the same number of coefficients as in the linear case. The characterization efficiency of the proposed exponential model is compared to the one provided by both 2-D linear and Volterra filters and the cooccurrence matrix method. The comparison is based on two criteria usually used to evaluate the features discriminating ability and the class quantification. Extensive experiments proved that the exponential model coefficients give better results in texture discrimination than several other parametric features even in a noisy context

Rotation-Invariant and scale-invariant steerable pyramid decomposition for texture image retrieval

This paper proposes a new rotation-invariant and scaleinvariant representation for texture image retrieval based on Steerable Pyramid Decomposition. By calculating the mean and standard deviation of decomposed image subbands, the texture feature vectors are extracted. To obtain rotation or scale invariance, the feature elements are aligned by considering either the dominant orientation or dominant scale of the input textures. Experiments were conducted on the Brodatz database aiming to compare our approach to the conventional Steerable Pyramid Decomposition, and a recent proposal for texture characteriztion based on Gabor Wavelets with regard to their retrieval effectiveness. Results demonstrate the superiority of the proposed method in rotated and scaled image datasets.

Uma ferramenta unificada para projeto, desenvolvimento, execução e recomendação de experimentos de aprendizado de máquina

Orientadores: Ricardo da Silva Torres, Anderson de Rezende RochaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Devido ao grande crescimento do uso de tecnologias para a aquisição de dados, temos que lidar com grandes e complexos conjuntos de dados a fim de extrair conhecimento que possa auxiliar o processo de tomada de decisão em diversos domínios de aplicação. Uma solução típica para abordar esta questão se baseia na utilização de métodos de aprendizado de máquina, que são métodos computacionais que extraem conhecimento útil a partir de experiências para melhorar o desempenho de aplicações-alvo. Existem diversas bibliotecas e arcabouços na literatura que oferecem apoio à execução de experimentos de aprendizado de máquina, no entanto, alguns não são flexíveis o suficiente para poderem ser estendidos com novos métodos, além de não oferecerem mecanismos que permitam o reuso de soluções de sucesso concebidos em experimentos anteriores na ferramenta. Neste trabalho, propomos um arcabouço para automatizar experimentos de aprendizado de máquina, oferecendo um ambiente padronizado baseado em workflow, tornando mais fácil a tarefa de avaliar diferentes descritores de características, classificadores e abordagens de fusão em uma ampla gama de tarefas. Também propomos o uso de medidas de similaridade e métodos de learning-to-rank em um cenário de recomendação, para que usuários possam ter acesso a soluções alternativas envolvendo experimentos de aprendizado de máquina. Nós realizamos experimentos com quatro medidas de similaridade (Jaccard, Sorensen, Jaro-Winkler e baseada em TF-IDF) e um método de learning-to-rank (LRAR) na tarefa de recomendar workflows modelados como uma sequência de atividades. Os resultados dos experimentos mostram que a medida Jaro-Winkler obteve o melhor desempenho, com resultados comparáveis aos observados para o método LRAR. Em ambos os casos, as recomendações realizadas são promissoras, e podem ajudar usuários reais em diferentes tarefas de aprendizado de máquinaAbstract: Due to the large growth of the use of technologies for data acquisition, we have to handle large and complex data sets in order to extract knowledge that can support the decision-making process in several domains. A typical solution for addressing this issue relies on the use of machine learning methods, which are computational methods that extract useful knowledge from experience to improve performance of target applications. There are several libraries and frameworks in the literature that support the execution of machine learning experiments. However, some of them are not flexible enough for being extended with novel methods and they do not support reusing of successful solutions devised in previous experiments made in the framework. In this work, we propose a framework for automating machine learning experiments that provides a workflow-based standardized environment and makes it easy to evaluate different feature descriptors, classifiers, and fusion approaches in a wide range of tasks. We also propose the use of similarity measures and learning-to-rank methods in a recommendation scenario, in which users may have access to alternative machine learning experiments. We performed experiments with four similarity measures (Jaccard, Sorensen, Jaro-Winkler, and a TF-IDF-based measure) and one learning-to-rank method (LRAR) in the task of recommending workflows modeled as a sequence of activities. Experimental results show that Jaro-Winkler yields the highest effectiveness performance with comparable results to those observed for LRAR. In both cases, the recommendations performed are very promising and might help real-world users in different daily machine learning tasksMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Extracting orientation and scale from smoothly varying textures with application to segmentation

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 109-112).The work in this thesis focuses on two main computer vision research topic: image segmentation and texture modeling. Information theoretic measures have been applied to image segmentation algorithms for the past decade. In previous work, common measures such as mutual information or J divergence have been used. Algorithms typically differ by the measure they use and the features they use to segment an image. When both the information measure and the features change, it is difficult to compare which algorithm actually performs better and for what reason. Though we do not provide a solution to this problem, we do compare and contrast three distances under two different measures. This thesis considers two forms of information theoretic based image segmentation algorithms that have previously been considered. We denote them here as the label method and the conditional method. Gradient ascent velocities are derived for a general Ali-Silvey distance for both methods, and a unique bijective mapping is shown to exist between the two methods when the Ali-Silvey distance takes on a specific form. While the conditional method is more commonly considered, it is implicitly limited by a two-region segmentation by construction. Using the derived mapping, one can easily extend a binary segmentation algorithm based on the conditional method to a multiregion segmentation algorithm based on the label method. The importance of initializations and local extrema is also considered, and a method of multiple random initializations is shown to produce better results.(cont.) Additionally, segmentation results and methods for comparing the utility of the different measures are presented. This thesis also considers a novel texture model for representing textured regions with smooth variations in orientation and scale. By utilizing the steerable pyramid of Simoncelli and Freeman, the textured regions of natural images are decomposed into explicit local attributes of contrast, bias, scale, and orientation. Once found, smoothness in these attributes are imposed via estimation of Markov random fields. This combination allows for demonstrable improvements in common scene analysis applications including segmentation, reflectance and shading estimation, and estimation of the radiometric response function from a single grayscale image.by Jason Chang.S.M

Content-based image retrieval based on relevance feedback and optimum-path forest classifier

Orientadores: Léo Pini Magalhães, Alexandre Xavier FalcãoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Com o crescente aumento de coleções de imagens resultantes da popularização da Internet e das câmeras digitais, métodos eficientes de busca tornam-se cada vez mais necessários. Neste contexto, esta tese propõe novos métodos de recuperação de imagens por conteúdo baseados em realimentação de relevância e no classificador por floresta de caminhos ótimos (OPF - Optimum-Path Forest), sendo também a primeira vez que o classificador OPF é utilizado em conjuntos de treinamento pequenos. Esta tese denomina como guloso e planejado os dois paradigmas distintos de aprendizagem por realimentação de relevância considerando as imagens retornadas. O primeiro paradigma tenta retornar a cada iteração sempre as imagens mais relevantes para o usuário, enquanto o segundo utiliza no aprendizado as imagens consideradas mais informativas ou difíceis de classificar. São apresentados os algoritmos de realimentação de relevância baseados em OPF utilizando ambos os paradigmas com descritor único. São utilizadas também duas técnicas de combinação de descritores juntamente com os métodos de realimentação de relevância baseados em OPF para melhorar a eficácia do processo de aprendizagem. A primeira, MSPS (Multi-Scale Parameter Search), é utilizada pela primeira vez em recuperação de imagens por conteúdo, enquanto a segunda é uma técnica consolidada baseada em programação genética. Uma nova abordagem para realimentação de relevância utilizando o classificador OPF em dois níveis de interesse é também apresentada. Nesta abordagem é possível, em um nível de interesse, selecionar os pixels nas imagens, além de escolher as imagens mais relevantes a cada iteração no outro nível. Esta tese mostra que o uso do classificador OPF para recuperação de imagens por conteúdo é muito eficiente e eficaz, necessitando de poucas iterações de aprendizado para apresentar os resultados desejados aos usuários. As simulações mostram que os métodos propostos superam os métodos de referência baseados em múltiplos pontos de consulta e em máquina de vetor de suporte (SVM). Além disso, os métodos propostos de busca de imagens baseados no classificador por floresta de caminhos ótimos mostraram ser em média 52 vezes mais rápidos do que os métodos baseados em máquina de vetor de suporteAbstract: Considering the increasing amount of image collections that result from popularization of the digital cameras and the Internet, efficient search methods are becoming increasingly necessary. In this context, this doctoral dissertation proposes new methods for content-based image retrieval based on relevance feedback and on the OPF (optimum-path forest) classifier, being also the first time that the OPF classifier is used in small training sets. This doctoral dissertation names as "greedy" and "planned" the two distinct learning paradigms for relevance feedback taking into account the returned images. The first paradigm attempts to return the images most relevant to the user at each iteration, while the second returns the images considered the most informative or difficult to be classified. The dissertation presents relevance feedback algorithms based on the OPF classifier using both paradigms with single descriptor. Two techniques for combining descriptors are also presented along with the relevance feedback methods based on OPF to improve the effectiveness of the learning process. The first one, MSPS (Multi-Scale Search Parameter), is used for the first time in content-based image retrieval and the second is a consolidated technique based on genetic programming. A new approach of relevance feedback using the OPF classifier at two levels of interest is also shown. In this approach it is possible to select the pixels in images at a level of interest and to choose the most relevant images at each iteration at another level. This dissertation shows that the use of the OPF classifier for content based image retrieval is very efficient and effective, requiring few learning iterations to produce the desired results to the users. Simulations show that the proposed methods outperform the reference methods based on multi-point query and support vector machine. Besides, the methods based on optimum-path forest have shown to be on the average 52 times faster than the SVM-based approachesDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric