Multi-scale gray level co-occurrence matrices for texture description

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Texture information plays an important role in image analysis. Although several descriptors have been proposed to extract and analyze texture, the development of automatic systems for image interpretation and object recognition is a difficult task due to the complex aspects of texture. Scale is an important information in texture analysis, since a same texture can be perceived as different texture patterns at distinct scales. Gray level co-occurrence matrices (GLCM) have been proved to be an effective texture descriptor. This paper presents a novel strategy for extending the GLCM to multiple scales through two different approaches, a Gaussian scale-space representation, which is constructed by smoothing the image with larger and larger low-pass filters producing a set of smoothed versions of the original image, and an image pyramid, which is defined by sampling the image both in space and scale. The performance of the proposed approach is evaluated by applying the multi-scale descriptor on five benchmark texture data sets and the results are compared to other well-known texture operators, including the original GLCM, that even though faster than the proposed method, is significantly outperformed in accuracy. (c) 2013 Elsevier B.V. All rights reserved.120SI336345Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação 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)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Evaluation of feature descriptors for texture classification

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Successful execution of tasks such as image classification, object detection and recognition, and scene classification depends on the definition of a set of features able to describe images effectively. Texture is among the features used by the human visual system. It provides information regarding spatial distribution, changes in brightness, and description regarding the structural arrangement of surfaces. However, although the visual human system is extremely accurate to recognize and describe textures, it is difficult to define a set of textural descriptors to be used in image analysis on different application domains. This work evaluates several texture descriptors and demonstrates that the combination of descriptors can improve the performance of texture classification. (C) 2012 SPIE and IS&T. [DOI: 10.1117/1.JEI.21.2.023016]212Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação 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)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)FAPESP [2010/10618-3